Through out this code i comment some code out cause it takes to long to run, so if you wanted to run this code, I have data already stored to a csv so the code that takes long to be run doesnt need to be run.¶
!pip uninstall spacy -y
!pip install spacy
!python -m spacy download en_core_web_sm
#!pip install spacy==2.1.1
#!pip install spacy textblob
!pip install matplotlib seaborn
#!pip install neuralcoref
!pip install --upgrade matplotlib networkx
Found existing installation: spacy 3.7.4
Uninstalling spacy-3.7.4:
Successfully uninstalled spacy-3.7.4
Collecting spacy
Using cached spacy-3.7.4-cp39-cp39-macosx_10_9_x86_64.whl (6.9 MB)
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Installing collected packages: spacy
Successfully installed spacy-3.7.4
Traceback (most recent call last):
File "/opt/anaconda3/lib/python3.9/runpy.py", line 188, in _run_module_as_main
mod_name, mod_spec, code = _get_module_details(mod_name, _Error)
File "/opt/anaconda3/lib/python3.9/runpy.py", line 147, in _get_module_details
return _get_module_details(pkg_main_name, error)
File "/opt/anaconda3/lib/python3.9/runpy.py", line 111, in _get_module_details
__import__(pkg_name)
File "/opt/anaconda3/lib/python3.9/site-packages/spacy/__init__.py", line 13, in <module>
from . import pipeline # noqa: F401
File "/opt/anaconda3/lib/python3.9/site-packages/spacy/pipeline/__init__.py", line 1, in <module>
from .attributeruler import AttributeRuler
File "/opt/anaconda3/lib/python3.9/site-packages/spacy/pipeline/attributeruler.py", line 8, in <module>
from ..language import Language
File "/opt/anaconda3/lib/python3.9/site-packages/spacy/language.py", line 43, in <module>
from .pipe_analysis import analyze_pipes, print_pipe_analysis, validate_attrs
File "/opt/anaconda3/lib/python3.9/site-packages/spacy/pipe_analysis.py", line 6, in <module>
from .tokens import Doc, Span, Token
File "/opt/anaconda3/lib/python3.9/site-packages/spacy/tokens/__init__.py", line 1, in <module>
from ._serialize import DocBin
File "/opt/anaconda3/lib/python3.9/site-packages/spacy/tokens/_serialize.py", line 14, in <module>
from ..vocab import Vocab
File "spacy/vocab.pyx", line 1, in init spacy.vocab
File "spacy/tokens/doc.pyx", line 49, in init spacy.tokens.doc
File "/opt/anaconda3/lib/python3.9/site-packages/spacy/schemas.py", line 287, in <module>
class TokenPattern(BaseModel):
File "pydantic/main.py", line 197, in pydantic.main.ModelMetaclass.__new__
File "pydantic/fields.py", line 506, in pydantic.fields.ModelField.infer
File "pydantic/fields.py", line 436, in pydantic.fields.ModelField.__init__
File "pydantic/fields.py", line 552, in pydantic.fields.ModelField.prepare
File "pydantic/fields.py", line 661, in pydantic.fields.ModelField._type_analysis
File "pydantic/fields.py", line 668, in pydantic.fields.ModelField._type_analysis
File "/opt/anaconda3/lib/python3.9/typing.py", line 847, in __subclasscheck__
return issubclass(cls, self.__origin__)
TypeError: issubclass() arg 1 must be a class
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import seaborn as sns
import matplotlib.pyplot as plt
from collections import Counter
from datetime import datetime
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
from textblob import TextBlob
import json
import pandas as pd
from nltk.corpus import state_union
import pandas as pd
#import neuralcoref
import networkx as nx
import matplotlib.pyplot as plt
extracting data from JSON file¶
Json file download from this website
with open('speeches.json', 'r') as file:
json_data = json.load(file)
# Convert JSON data to a string
json_string = json.dumps(json_data, indent=4) # indent is optional, for pretty-printing
# Load JSON data
with open('speeches.json', 'r') as file:
data = json.load(file)
# Normalize JSON data
normalized_data = pd.json_normalize(data) # This assumes 'data' is a list of records
# Create DataFrame
df = pd.DataFrame(normalized_data)
# Sort the DataFrame by the 'date' column
df_sorted = df.sort_values(by='date')
df_sorted.sample()
| doc_name | date | transcript | president | title | |
|---|---|---|---|---|---|
| 306 | october-31-1936-speech-madison-square-garden | 1936-10-31 | Senator Wagner, Governor Lehman, ladies and ge... | Franklin D. Roosevelt | October 31, 1936: Speech at Madison Square Garden |
Adding in speeches not included like speeches done by Eisenhower and Truman¶
This code is taken from Nicole Semerano's project in the Presidential_NLP.ipynb file
https://github.com/nicolesemerano/Metis-Project-4-Presidential-Speeches-NLP/tree/master
import nltk
# Download the required NLTK corpora
nltk.download('state_union')
# Now, you can safely load and use the 'state_union' corpus
from nltk.corpus import state_union
[nltk_data] Downloading package state_union to [nltk_data] /Users/brukeamare/nltk_data... [nltk_data] Package state_union is already up-to-date!
sOu = dict() #sOu=state Of the union
#Truman's 1945 and '46 speeches had different formats that the rest
truman45 = state_union.raw('1945-Truman.txt')
truman45 = truman45.strip('PRESIDENT')
title45 = 'STATE OF THE UNION'
name_t = truman45.split('\n', 3)[0][1:16]
name45 = name_t.title()
date45 = truman45.split('\n', 3)[2][0:16]
key45 = title45 + " " + date45
speech45 = truman45.split('\n', 3)[3]
sOu[key45] = [name45] + [date45] + [speech45]
truman46 = state_union.raw('1946-Truman.txt')
truman46 = truman46.strip('PRESIDENT')
title46 = 'STATE OF THE UNION'
name_t = truman46.split('\n', 3)[0][1:16]
name46 = name_t.title()
date46 = truman46.split('\n', 3)[2][0:16]
key46 = title46 + " " + date46
speech46 = truman46.split('\n', 3)[3]
sOu[key46] = [name46] + [date46] + [speech46]
#These Truman files have the same format, so grouped together
txt_files_T = ['1947-Truman.txt', '1948-Truman.txt',
'1949-Truman.txt', '1950-Truman.txt', '1951-Truman.txt']
for text in txt_files_T:
x = state_union.raw(text)
x = x.strip('PRESIDENT')
k = x.split('\n', 3)[0][-18:]
name1 = x.split('\n', 3)[0][1:16]
name = name1.title()
date = x.split('\n', 3)[2]
key1 = k + " " + date
speech = x.split('\n', 3)[3]
sOu[key1] = [name] + [date] + [speech]
#These Eisenhower files have two types of formats, so grouped together
txt_files_E = ['1953-Eisenhower.txt', '1954-Eisenhower.txt', '1955-Eisenhower.txt']
for text in txt_files_E:
x = state_union.raw(text)
x = x.strip('PRESIDENT')
k = x.split('\n', 3)[0][-18:]
name1 = x.split('\n', 3)[0][1:-59]
name = name1.title()
date = x.split('\n', 3)[2]
key1 = k + " " + date
speech = x.split('\n', 3)[3]
sOu[key1] = [name] + [date] + [speech]
no_date = ['1956-Eisenhower.txt', '1957-Eisenhower.txt', '1958-Eisenhower.txt',
'1959-Eisenhower.txt', '1960-Eisenhower.txt']
for text in no_date:
x = state_union.raw(text)
x = x.strip('PRESIDENT')
k = x.split('\n', 2)[0][-18:]
name1 = x.split('\n', 2)[0][1:-59]
name = name1.title()
date = text[:4]
key1 = k + " " + date
speech = x.split('\n', 2)[2]
sOu[key1] = [name] + [date] + [speech]
sOu.keys()
dict_keys(['STATE OF THE UNION April 16, 1945', 'STATE OF THE UNION January 21, 1946', 'STATE OF THE UNION January 6, 1947', 'STATE OF THE UNION January 7, 1948', 'STATE OF THE UNION January 5, 1949', 'STATE OF THE UNION January 4, 1950', 'STATE OF THE UNION January 8, 1951', 'STATE OF THE UNION February 2, 1953', 'STATE OF THE UNION January 7, 1954', 'STATE OF THE UNION January 6, 1955', 'STATE OF THE UNION 1956', 'STATE OF THE UNION 1957', 'STATE OF THE UNION 1958', 'STATE OF THE UNION 1959', 'STATE OF THE UNION 1960'])
df_dict2 = pd.DataFrame(sOu)
df50s = df_dict2.T
df50s.columns = ['President', 'Date', 'Speech']
df50s.sample()
| President | Date | Speech | |
|---|---|---|---|
| STATE OF THE UNION 1957 | Dwight D. Eisenhower | 1957 | I appear before the Congress today to report o... |
Nicole Semerano's code stops here.
df50s.rename(columns={'Date': 'date'}, inplace=True)
df50s.rename(columns={'President': 'president'}, inplace=True)
df50s.rename(columns={'Speech': 'transcript'}, inplace=True)
# Rename the index
df50s.index.name = 'title'
df50s.reset_index(inplace=True)
df_sorted = df_sorted.reset_index(drop=True)
df_final = pd.concat([df_sorted, df50s], axis=0)
df_sorted_final = df_final.sort_values(by='date')
# Convert DataFrame to CSV
df_sorted_final.to_csv('output_filename.csv', index=False)
df_sorted_final['Year'] = df_sorted_final.date.str[:4]
df_sorted_final
| doc_name | date | transcript | president | title | Year | |
|---|---|---|---|---|---|---|
| 0 | april-30-1789-first-inaugural-address | 1789-04-30 | Fellow Citizens of the Senate and the House of... | George Washington | April 30, 1789: First Inaugural Address | 1789 |
| 1 | october-3-1789-thanksgiving-proclamation | 1789-10-03 | Whereas it is the duty of all Nations to ackno... | George Washington | October 3, 1789: Thanksgiving Proclamation | 1789 |
| 2 | january-8-1790-first-annual-message-congress | 1790-01-08 | Fellow Citizens of the Senate and House of Rep... | George Washington | January 8, 1790: First Annual Message to Congress | 1790 |
| 3 | december-8-1790-second-annual-message-congress | 1790-12-08 | Fellow citizens of the Senate and House of Re... | George Washington | December 8, 1790: Second Annual Message to Con... | 1790 |
| 4 | december-29-1790-talk-chiefs-and-counselors-se... | 1790-12-29 | I the President of the United States, by my o... | George Washington | December 29, 1790: Talk to the Chiefs and Coun... | 1790 |
| ... | ... | ... | ... | ... | ... | ... |
| 2 | NaN | January 6, 1947 | \nMr. President, Mr. Speaker, Members of the C... | Harry S. Truman | STATE OF THE UNION January 6, 1947 | Janu |
| 9 | NaN | January 6, 1955 | \nMr. President, Mr. Speaker, Members of the C... | Dwight D. Eisenhower | STATE OF THE UNION January 6, 1955 | Janu |
| 3 | NaN | January 7, 1948 | \nMr. President, Mr. Speaker, and Members of t... | Harry S. Truman | STATE OF THE UNION January 7, 1948 | Janu |
| 8 | NaN | January 7, 1954 | \nMr. President, Mr. Speaker, Members of the E... | Dwight D. Eisenhower | STATE OF THE UNION January 7, 1954 | Janu |
| 6 | NaN | January 8, 1951 | \nMr. President, Mr. Speaker, Members Of the C... | Harry S. Truman | STATE OF THE UNION January 8, 1951 | Janu |
1065 rows × 6 columns
df_sorted_final.to_csv('output_filename.csv', index=False)
SO the data collection is done now, no doing some data cleaning¶
df_sorted_final= pd.read_csv('output_filename.csv')
df_sorted_final
| doc_name | date | transcript | president | title | Year | |
|---|---|---|---|---|---|---|
| 0 | april-30-1789-first-inaugural-address | 1789-04-30 | Fellow Citizens of the Senate and the House of... | George Washington | April 30, 1789: First Inaugural Address | 1789 |
| 1 | october-3-1789-thanksgiving-proclamation | 1789-10-03 | Whereas it is the duty of all Nations to ackno... | George Washington | October 3, 1789: Thanksgiving Proclamation | 1789 |
| 2 | january-8-1790-first-annual-message-congress | 1790-01-08 | Fellow Citizens of the Senate and House of Rep... | George Washington | January 8, 1790: First Annual Message to Congress | 1790 |
| 3 | december-8-1790-second-annual-message-congress | 1790-12-08 | Fellow citizens of the Senate and House of Re... | George Washington | December 8, 1790: Second Annual Message to Con... | 1790 |
| 4 | december-29-1790-talk-chiefs-and-counselors-se... | 1790-12-29 | I the President of the United States, by my o... | George Washington | December 29, 1790: Talk to the Chiefs and Coun... | 1790 |
| ... | ... | ... | ... | ... | ... | ... |
| 1060 | NaN | January 6, 1947 | \nMr. President, Mr. Speaker, Members of the C... | Harry S. Truman | STATE OF THE UNION January 6, 1947 | Janu |
| 1061 | NaN | January 6, 1955 | \nMr. President, Mr. Speaker, Members of the C... | Dwight D. Eisenhower | STATE OF THE UNION January 6, 1955 | Janu |
| 1062 | NaN | January 7, 1948 | \nMr. President, Mr. Speaker, and Members of t... | Harry S. Truman | STATE OF THE UNION January 7, 1948 | Janu |
| 1063 | NaN | January 7, 1954 | \nMr. President, Mr. Speaker, Members of the E... | Dwight D. Eisenhower | STATE OF THE UNION January 7, 1954 | Janu |
| 1064 | NaN | January 8, 1951 | \nMr. President, Mr. Speaker, Members Of the C... | Harry S. Truman | STATE OF THE UNION January 8, 1951 | Janu |
1065 rows × 6 columns
samp=df_sorted_final.loc[:, ['doc_name', "date", "transcript",'president','title', 'Year']]
# Assuming 'df' is your DataFrame
index_to_remove1 = samp.index[-2] # Get the index of the second-to-last row
# Remove the second-to-last row cause this speech is alraady in the data
samp = samp.drop(index_to_remove1)
# Remove the second-to-last row
index_to_remove2 = samp.index[-3]
samp = samp.drop(index_to_remove2)
# Remove the second-to-last row
index_to_remove4 = samp.index[-7]
samp = samp.drop(index_to_remove4)
# Remove the second-to-last row
index_to_remove3 = samp.index[-7]
samp = samp.drop(index_to_remove3)
samp[-7:]
#samp.to_csv('samp_filename.csv', index=False)
| doc_name | date | transcript | president | title | Year | |
|---|---|---|---|---|---|---|
| 1054 | february-21-2023-remarks-one-year-anniversary-... | 2023-02-22 | THE PRESIDENT: Hello, Poland! One of our grea... | Joe Biden | February 21, 2023: Remarks on the One-Year Ann... | 2023 |
| 1057 | NaN | January 21, 1946 | \nTo the Congress of the United States:\nA qua... | Harry S. Truman | STATE OF THE UNION January 21, 1946 | Janu |
| 1058 | NaN | January 4, 1950 | \nMr. President, Mr. Speaker, Members of the C... | Harry S. Truman | STATE OF THE UNION January 4, 1950 | Janu |
| 1059 | NaN | January 5, 1949 | \nMr. President, Mr. Speaker, Members of the C... | Harry S. Truman | STATE OF THE UNION January 5, 1949 | Janu |
| 1060 | NaN | January 6, 1947 | \nMr. President, Mr. Speaker, Members of the C... | Harry S. Truman | STATE OF THE UNION January 6, 1947 | Janu |
| 1062 | NaN | January 7, 1948 | \nMr. President, Mr. Speaker, and Members of t... | Harry S. Truman | STATE OF THE UNION January 7, 1948 | Janu |
| 1064 | NaN | January 8, 1951 | \nMr. President, Mr. Speaker, Members Of the C... | Harry S. Truman | STATE OF THE UNION January 8, 1951 | Janu |
samp.reset_index(inplace=True)
samp.drop('index', axis=1, inplace=True)
fukkk=samp[-12:]
fukkk
| doc_name | date | transcript | president | title | Year | |
|---|---|---|---|---|---|---|
| 1049 | march-26-2022-remarks-support-people-ukraine | 2022-03-26 | THE PRESIDENT: Thank you, thank you, thank you... | Joe Biden | March 26, 2022: Remarks in Support of the Peop... | 2022 |
| 1050 | may-24-2022-remarks-school-shooting-uvalde-texas | 2022-05-25 | Good evening, fellow Americans.\r\n\r\nI had h... | Joe Biden | May 24, 2022: Remarks on School Shooting in Uv... | 2022 |
| 1051 | september-1-2022-remarks-continued-battle-soul... | 2022-09-02 | THE PRESIDENT: My fellow Americans, please, if... | Joe Biden | September 1, 2022: Remarks on the Continued Ba... | 2022 |
| 1052 | september-21-2022-speech-77th-session-united-n... | 2022-09-21 | Thank you. \r\n\r\nMr. President, Mr. Secretar... | Joe Biden | September 21, 2022: Speech before the 77th Ses... | 2022 |
| 1053 | february-7-2023-state-union-address | 2023-02-08 | Mr. Speaker. Madam Vice President. Our First L... | Joe Biden | February 7, 2023: State of the Union Address | 2023 |
| 1054 | february-21-2023-remarks-one-year-anniversary-... | 2023-02-22 | THE PRESIDENT: Hello, Poland! One of our grea... | Joe Biden | February 21, 2023: Remarks on the One-Year Ann... | 2023 |
| 1055 | NaN | January 21, 1946 | \nTo the Congress of the United States:\nA qua... | Harry S. Truman | STATE OF THE UNION January 21, 1946 | Janu |
| 1056 | NaN | January 4, 1950 | \nMr. President, Mr. Speaker, Members of the C... | Harry S. Truman | STATE OF THE UNION January 4, 1950 | Janu |
| 1057 | NaN | January 5, 1949 | \nMr. President, Mr. Speaker, Members of the C... | Harry S. Truman | STATE OF THE UNION January 5, 1949 | Janu |
| 1058 | NaN | January 6, 1947 | \nMr. President, Mr. Speaker, Members of the C... | Harry S. Truman | STATE OF THE UNION January 6, 1947 | Janu |
| 1059 | NaN | January 7, 1948 | \nMr. President, Mr. Speaker, and Members of t... | Harry S. Truman | STATE OF THE UNION January 7, 1948 | Janu |
| 1060 | NaN | January 8, 1951 | \nMr. President, Mr. Speaker, Members Of the C... | Harry S. Truman | STATE OF THE UNION January 8, 1951 | Janu |
from datetime import datetime
for i in range(6):
j=i+1
um=samp.loc[(1054+j),'date']
date_object = datetime.strptime(um, '%B %d, %Y')
new_date_string = date_object.strftime('%Y-%m-%d')
samp.loc[(1054+j),'date']=new_date_string
samp[-6:]
| doc_name | date | transcript | president | title | Year | |
|---|---|---|---|---|---|---|
| 1055 | NaN | 1946-01-21 | \nTo the Congress of the United States:\nA qua... | Harry S. Truman | STATE OF THE UNION January 21, 1946 | Janu |
| 1056 | NaN | 1950-01-04 | \nMr. President, Mr. Speaker, Members of the C... | Harry S. Truman | STATE OF THE UNION January 4, 1950 | Janu |
| 1057 | NaN | 1949-01-05 | \nMr. President, Mr. Speaker, Members of the C... | Harry S. Truman | STATE OF THE UNION January 5, 1949 | Janu |
| 1058 | NaN | 1947-01-06 | \nMr. President, Mr. Speaker, Members of the C... | Harry S. Truman | STATE OF THE UNION January 6, 1947 | Janu |
| 1059 | NaN | 1948-01-07 | \nMr. President, Mr. Speaker, and Members of t... | Harry S. Truman | STATE OF THE UNION January 7, 1948 | Janu |
| 1060 | NaN | 1951-01-08 | \nMr. President, Mr. Speaker, Members Of the C... | Harry S. Truman | STATE OF THE UNION January 8, 1951 | Janu |
samp_sorted = samp.sort_values(by='date')
samp_sorted.reset_index(inplace=True)
samp_sorted['Year'] = samp_sorted.date.str[:4]
samp_sorted.drop('index', axis=1, inplace=True)
samp_sorted
| doc_name | date | transcript | president | title | Year | |
|---|---|---|---|---|---|---|
| 0 | april-30-1789-first-inaugural-address | 1789-04-30 | Fellow Citizens of the Senate and the House of... | George Washington | April 30, 1789: First Inaugural Address | 1789 |
| 1 | october-3-1789-thanksgiving-proclamation | 1789-10-03 | Whereas it is the duty of all Nations to ackno... | George Washington | October 3, 1789: Thanksgiving Proclamation | 1789 |
| 2 | january-8-1790-first-annual-message-congress | 1790-01-08 | Fellow Citizens of the Senate and House of Rep... | George Washington | January 8, 1790: First Annual Message to Congress | 1790 |
| 3 | december-8-1790-second-annual-message-congress | 1790-12-08 | Fellow citizens of the Senate and House of Re... | George Washington | December 8, 1790: Second Annual Message to Con... | 1790 |
| 4 | december-29-1790-talk-chiefs-and-counselors-se... | 1790-12-29 | I the President of the United States, by my o... | George Washington | December 29, 1790: Talk to the Chiefs and Coun... | 1790 |
| ... | ... | ... | ... | ... | ... | ... |
| 1056 | may-24-2022-remarks-school-shooting-uvalde-texas | 2022-05-25 | Good evening, fellow Americans.\r\n\r\nI had h... | Joe Biden | May 24, 2022: Remarks on School Shooting in Uv... | 2022 |
| 1057 | september-1-2022-remarks-continued-battle-soul... | 2022-09-02 | THE PRESIDENT: My fellow Americans, please, if... | Joe Biden | September 1, 2022: Remarks on the Continued Ba... | 2022 |
| 1058 | september-21-2022-speech-77th-session-united-n... | 2022-09-21 | Thank you. \r\n\r\nMr. President, Mr. Secretar... | Joe Biden | September 21, 2022: Speech before the 77th Ses... | 2022 |
| 1059 | february-7-2023-state-union-address | 2023-02-08 | Mr. Speaker. Madam Vice President. Our First L... | Joe Biden | February 7, 2023: State of the Union Address | 2023 |
| 1060 | february-21-2023-remarks-one-year-anniversary-... | 2023-02-22 | THE PRESIDENT: Hello, Poland! One of our grea... | Joe Biden | February 21, 2023: Remarks on the One-Year Ann... | 2023 |
1061 rows × 6 columns
samp_sorted.to_csv('outputfinal_filename.csv', index=False)
samp_sorted= pd.read_csv('outputfinal_filename.csv')
samp_sorted
| doc_name | date | transcript | president | title | Year | |
|---|---|---|---|---|---|---|
| 0 | april-30-1789-first-inaugural-address | 1789-04-30 | Fellow Citizens of the Senate and the House of... | George Washington | April 30, 1789: First Inaugural Address | 1789 |
| 1 | october-3-1789-thanksgiving-proclamation | 1789-10-03 | Whereas it is the duty of all Nations to ackno... | George Washington | October 3, 1789: Thanksgiving Proclamation | 1789 |
| 2 | january-8-1790-first-annual-message-congress | 1790-01-08 | Fellow Citizens of the Senate and House of Rep... | George Washington | January 8, 1790: First Annual Message to Congress | 1790 |
| 3 | december-8-1790-second-annual-message-congress | 1790-12-08 | Fellow citizens of the Senate and House of Re... | George Washington | December 8, 1790: Second Annual Message to Con... | 1790 |
| 4 | december-29-1790-talk-chiefs-and-counselors-se... | 1790-12-29 | I the President of the United States, by my o... | George Washington | December 29, 1790: Talk to the Chiefs and Coun... | 1790 |
| ... | ... | ... | ... | ... | ... | ... |
| 1056 | may-24-2022-remarks-school-shooting-uvalde-texas | 2022-05-25 | Good evening, fellow Americans.\r\n\r\nI had h... | Joe Biden | May 24, 2022: Remarks on School Shooting in Uv... | 2022 |
| 1057 | september-1-2022-remarks-continued-battle-soul... | 2022-09-02 | THE PRESIDENT: My fellow Americans, please, if... | Joe Biden | September 1, 2022: Remarks on the Continued Ba... | 2022 |
| 1058 | september-21-2022-speech-77th-session-united-n... | 2022-09-21 | Thank you. \r\n\r\nMr. President, Mr. Secretar... | Joe Biden | September 21, 2022: Speech before the 77th Ses... | 2022 |
| 1059 | february-7-2023-state-union-address | 2023-02-08 | Mr. Speaker. Madam Vice President. Our First L... | Joe Biden | February 7, 2023: State of the Union Address | 2023 |
| 1060 | february-21-2023-remarks-one-year-anniversary-... | 2023-02-22 | THE PRESIDENT: Hello, Poland! One of our grea... | Joe Biden | February 21, 2023: Remarks on the One-Year Ann... | 2023 |
1061 rows × 6 columns
Not part of My code from Spring, NICOLE SEMERANO'S code¶
zetemp=samp_sorted
!pip install selenium
from sklearn.cluster import KMeans
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from bs4 import BeautifulSoup
import requests
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
import time, os
import random
import pickle
from nltk.probability import FreqDist
from nltk.corpus import state_union
import re
import string
from nltk.stem import LancasterStemmer
nltk.download('state_union')
Requirement already satisfied: selenium in /opt/anaconda3/lib/python3.9/site-packages (4.15.2) Requirement already satisfied: trio-websocket~=0.9 in /opt/anaconda3/lib/python3.9/site-packages (from selenium) (0.11.1) Requirement already satisfied: certifi>=2021.10.8 in /opt/anaconda3/lib/python3.9/site-packages (from selenium) (2021.10.8) Requirement already satisfied: trio~=0.17 in /opt/anaconda3/lib/python3.9/site-packages (from selenium) (0.23.1) Requirement already satisfied: urllib3[socks]<3,>=1.26 in /opt/anaconda3/lib/python3.9/site-packages (from selenium) (1.26.7) Requirement already satisfied: exceptiongroup>=1.0.0rc9 in /opt/anaconda3/lib/python3.9/site-packages (from trio~=0.17->selenium) (1.2.0) Requirement already satisfied: sniffio>=1.3.0 in /opt/anaconda3/lib/python3.9/site-packages (from trio~=0.17->selenium) (1.3.0) Requirement already satisfied: outcome in /opt/anaconda3/lib/python3.9/site-packages (from trio~=0.17->selenium) (1.3.0.post0) Requirement already satisfied: idna in /opt/anaconda3/lib/python3.9/site-packages (from trio~=0.17->selenium) (3.2) Requirement already satisfied: sortedcontainers in /opt/anaconda3/lib/python3.9/site-packages (from trio~=0.17->selenium) (2.4.0) Requirement already satisfied: attrs>=20.1.0 in /opt/anaconda3/lib/python3.9/site-packages (from trio~=0.17->selenium) (21.2.0) Requirement already satisfied: wsproto>=0.14 in /opt/anaconda3/lib/python3.9/site-packages (from trio-websocket~=0.9->selenium) (1.2.0) Requirement already satisfied: PySocks!=1.5.7,<2.0,>=1.5.6 in /opt/anaconda3/lib/python3.9/site-packages (from urllib3[socks]<3,>=1.26->selenium) (1.7.1) Requirement already satisfied: h11<1,>=0.9.0 in /opt/anaconda3/lib/python3.9/site-packages (from wsproto>=0.14->trio-websocket~=0.9->selenium) (0.14.0)
[nltk_data] Downloading package state_union to [nltk_data] /Users/brukeamare/nltk_data... [nltk_data] Package state_union is already up-to-date!
True
zetemp.sort_values(by=['Year'], inplace=True)
zetemp.head(3)
# Text preprocessing steps - remove numbers, captial letters and punctuation
alphanumeric = lambda x: re.sub('\w*\d\w*', ' ', x)
punc_lower = lambda x: re.sub('[%s]' % re.escape(string.punctuation), ' ', x.lower())
no_n = lambda x: re.sub('\n', '', x)
no_r = lambda x: re.sub('\r', '', x)
no_hyphen = lambda x: re.sub('-', ' ', x)
zetemp['transcript'] = zetemp.transcript.map(alphanumeric).map(punc_lower).map(no_n).map(no_r).map(no_hyphen)
zetemp['Split'] = zetemp.transcript.str.split()
zetemp.head(3)
stemmer = LancasterStemmer()
zetemp['Stemmed'] = zetemp['Split'].apply(lambda x: [stemmer.stem(y) for y in x])# Stem every word.
zetemp.sample(3)
zetemp.to_csv('Presidents_Speeches.csv')
zetemp.to_pickle("my_Presidents.pkl")
with open('my_Presidents.pkl', 'wb') as picklefile:
pickle.dump(zetemp, picklefile)
with open("my_Presidents.pkl", 'rb') as picklefile:
my_old_df = pickle.load(picklefile)
zetemp = my_old_df
zetemp.reset_index(level = 0, inplace = True)
zetemp = df.rename(columns = {'index':'Title'})
zetemp.head(3)
| doc_name | date | transcript | president | title | |
|---|---|---|---|---|---|
| 0 | april-18-1977-address-nation-energy | 1977-04-18 | Good evening.\r\nTonight I want to have an unp... | Jimmy Carter | April 18, 1977: Address to the Nation on Energy |
| 1 | april-25-1980-statement-iran-rescue-mission | 1980-04-25 | Late yesterday, I cancelled a carefully planne... | Jimmy Carter | April 25, 1980: Statement on the Iran Rescue M... |
| 2 | august-14-1980-acceptance-speech-democratic-na... | 1980-08-14 | Fellow Democrats, fellow citizens:\r\n\r\nI th... | Jimmy Carter | August 14, 1980: Acceptance Speech at the Demo... |
zetemp.president.value_counts()
Lyndon B. Johnson 71 Ronald Reagan 60 Barack Obama 51 Franklin D. Roosevelt 49 John F. Kennedy 45 Donald Trump 43 George W. Bush 40 Bill Clinton 39 Woodrow Wilson 33 Ulysses S. Grant 32 Andrew Johnson 31 Grover Cleveland 30 Herbert Hoover 30 Andrew Jackson 26 James K. Polk 25 Thomas Jefferson 24 George H. W. Bush 23 Richard M. Nixon 23 Benjamin Harrison 23 Jimmy Carter 22 James Madison 22 Theodore Roosevelt 22 Joe Biden 21 George Washington 21 Harry S. Truman 19 Warren G. Harding 18 John Tyler 18 Rutherford B. Hayes 16 Abraham Lincoln 15 Dwight D. Eisenhower 15 Franklin Pierce 15 William McKinley 14 Gerald Ford 14 James Buchanan 14 Calvin Coolidge 12 William Taft 12 Chester A. Arthur 11 Martin Van Buren 10 James Monroe 10 John Adams 9 John Quincy Adams 9 Millard Fillmore 7 Zachary Taylor 4 James A. Garfield 1 William Harrison 1 Name: president, dtype: int64
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
plt.figure(figsize=(8, 14))
my_colors = ['orange','orange','orange','orange','orange','orange', 'darkviolet', 'darkviolet', 'orange', 'orange','darkviolet', 'orange',
'orange', 'orange', 'darkviolet','orange', 'orange','orange', 'orange', 'orange', 'darkviolet', 'darkviolet', 'orange','darkviolet',
'orange','darkviolet', 'orange', 'orange','darkviolet', 'darkviolet','orange','darkviolet', 'orange', 'darkviolet',
'orange', 'darkviolet','darkviolet','darkviolet','darkviolet','orange', 'orange', 'darkviolet','darkviolet','darkviolet','darkviolet']
df.president.value_counts(ascending=True).plot(kind = 'barh', color=my_colors)
plt.xlabel('Number of Speeches', fontsize = 15)
plt.ylabel('Presidents', fontsize = 16)
plt.title('Number of Speeches per President ', fontsize = 20)
plt.tight_layout()
plt.savefig('No._Pres_Speeches.pdf');
NICOLE'S CODE STOPS HERE¶
SO the data collection is done now, now doing NLP PROCESSING¶
Named Entity Recognition¶
Data processing¶
samp_sorted= pd.read_csv('outputfinal_filename.csv')
samp_sorted
| doc_name | date | transcript | president | title | Year | |
|---|---|---|---|---|---|---|
| 0 | april-30-1789-first-inaugural-address | 1789-04-30 | Fellow Citizens of the Senate and the House of... | George Washington | April 30, 1789: First Inaugural Address | 1789 |
| 1 | october-3-1789-thanksgiving-proclamation | 1789-10-03 | Whereas it is the duty of all Nations to ackno... | George Washington | October 3, 1789: Thanksgiving Proclamation | 1789 |
| 2 | january-8-1790-first-annual-message-congress | 1790-01-08 | Fellow Citizens of the Senate and House of Rep... | George Washington | January 8, 1790: First Annual Message to Congress | 1790 |
| 3 | december-8-1790-second-annual-message-congress | 1790-12-08 | Fellow citizens of the Senate and House of Re... | George Washington | December 8, 1790: Second Annual Message to Con... | 1790 |
| 4 | december-29-1790-talk-chiefs-and-counselors-se... | 1790-12-29 | I the President of the United States, by my o... | George Washington | December 29, 1790: Talk to the Chiefs and Coun... | 1790 |
| ... | ... | ... | ... | ... | ... | ... |
| 1056 | may-24-2022-remarks-school-shooting-uvalde-texas | 2022-05-25 | Good evening, fellow Americans.\r\n\r\nI had h... | Joe Biden | May 24, 2022: Remarks on School Shooting in Uv... | 2022 |
| 1057 | september-1-2022-remarks-continued-battle-soul... | 2022-09-02 | THE PRESIDENT: My fellow Americans, please, if... | Joe Biden | September 1, 2022: Remarks on the Continued Ba... | 2022 |
| 1058 | september-21-2022-speech-77th-session-united-n... | 2022-09-21 | Thank you. \r\n\r\nMr. President, Mr. Secretar... | Joe Biden | September 21, 2022: Speech before the 77th Ses... | 2022 |
| 1059 | february-7-2023-state-union-address | 2023-02-08 | Mr. Speaker. Madam Vice President. Our First L... | Joe Biden | February 7, 2023: State of the Union Address | 2023 |
| 1060 | february-21-2023-remarks-one-year-anniversary-... | 2023-02-22 | THE PRESIDENT: Hello, Poland! One of our grea... | Joe Biden | February 21, 2023: Remarks on the One-Year Ann... | 2023 |
1061 rows × 6 columns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
df= pd.read_csv('outputfinal_filename.csv')
df
| doc_name | date | transcript | president | title | Year | |
|---|---|---|---|---|---|---|
| 0 | april-30-1789-first-inaugural-address | 1789-04-30 | Fellow Citizens of the Senate and the House of... | George Washington | April 30, 1789: First Inaugural Address | 1789 |
| 1 | october-3-1789-thanksgiving-proclamation | 1789-10-03 | Whereas it is the duty of all Nations to ackno... | George Washington | October 3, 1789: Thanksgiving Proclamation | 1789 |
| 2 | january-8-1790-first-annual-message-congress | 1790-01-08 | Fellow Citizens of the Senate and House of Rep... | George Washington | January 8, 1790: First Annual Message to Congress | 1790 |
| 3 | december-8-1790-second-annual-message-congress | 1790-12-08 | Fellow citizens of the Senate and House of Re... | George Washington | December 8, 1790: Second Annual Message to Con... | 1790 |
| 4 | december-29-1790-talk-chiefs-and-counselors-se... | 1790-12-29 | I the President of the United States, by my o... | George Washington | December 29, 1790: Talk to the Chiefs and Coun... | 1790 |
| ... | ... | ... | ... | ... | ... | ... |
| 1056 | may-24-2022-remarks-school-shooting-uvalde-texas | 2022-05-25 | Good evening, fellow Americans.\r\n\r\nI had h... | Joe Biden | May 24, 2022: Remarks on School Shooting in Uv... | 2022 |
| 1057 | september-1-2022-remarks-continued-battle-soul... | 2022-09-02 | THE PRESIDENT: My fellow Americans, please, if... | Joe Biden | September 1, 2022: Remarks on the Continued Ba... | 2022 |
| 1058 | september-21-2022-speech-77th-session-united-n... | 2022-09-21 | Thank you. \r\n\r\nMr. President, Mr. Secretar... | Joe Biden | September 21, 2022: Speech before the 77th Ses... | 2022 |
| 1059 | february-7-2023-state-union-address | 2023-02-08 | Mr. Speaker. Madam Vice President. Our First L... | Joe Biden | February 7, 2023: State of the Union Address | 2023 |
| 1060 | february-21-2023-remarks-one-year-anniversary-... | 2023-02-22 | THE PRESIDENT: Hello, Poland! One of our grea... | Joe Biden | February 21, 2023: Remarks on the One-Year Ann... | 2023 |
1061 rows × 6 columns
# Count the number of words in each transcript
df['word_count'] = df['transcript'].str.split().str.len()
# Group by decade (assuming 'Year' column is the last 4 digits of the 'date' column)
df['decade'] = df['date'].str[:4].astype(int) // 10 * 10
# Increase font size and set a single color
sns.set(style='whitegrid')
plt.figure(figsize=(20, 10))
sns.boxplot(x='decade', y='word_count', data=df, color='lightblue') # Adjust the color as needed
# Customize plot appearance
plt.title('Word Count of Presidential Speeches Over Decades', fontsize=24)
plt.xlabel('Decade', fontsize=18)
plt.ylabel('Word Count', fontsize=18)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.xticks(rotation=45) # Rotate x labels if needed
# Show the plot
plt.show()
litz=samp_sorted.loc[:, ["Year", "transcript"]]
fr=litz["Year"].drop_duplicates()
dates=[]
for i in fr:
dates.append(i)
Case Study 1¶
samp_sorted= pd.read_csv('outputfinal_filename.csv')
startyear=1939
endyear=1945
startDate=19390901 # Invasion of Poland, attack on Poland by Nazi Germany that marked the start of World War II. The invasion lasted from September 1 to October 5, 1939. As dawn broke on September 1, 1939, German forces launched a surprise attack on Poland.
endDate=19450814 # US President Harry S. Truman announced Japan's surrender and the end of World War II
gents = [
"Europe", "Germany", "Hitler", "Jews",
"World War II", 'Navy', 'Army', 'Poland', 'Japan', 'Japanese'
]
bummy = samp_sorted.loc[(samp_sorted['Year'] >= startyear) & (samp_sorted['Year'] <= endyear)]
bummy.reset_index(inplace=True)
bummy.drop('index', axis=1, inplace=True)
df1 = pd.DataFrame(columns=['doc_name', 'date', 'transcript', 'president', 'title', 'Year'])
for i in range(len(bummy)):
date_string = bummy.loc[bummy.index[i], 'date'] # Accessing the 'date' column for each row
cleaned_string = date_string.replace('-', '')
# Convert string to number
result = int(cleaned_string)
if startDate <= result <= endDate:
# Assuming df1 is the DataFrame you want to add a row to, and df2 is the DataFrame from which you want to add the row
row_to_add = bummy.loc[i] # Select the row you want to add from df2
df1.loc[len(df1)] = row_to_add.values
# Ensure the 'date' column is in datetime format
df1['date'] = pd.to_datetime(df1['date'])
# Create a DataFrame with each president's first and last speech date
president_dates = df1.groupby('president')['date'].agg(['min', 'max']).reset_index()
president_dates.columns = ['president', 'first_speech', 'last_speech']
# Print the new DataFrame
print(president_dates)
# Create an empty DataFrame to store the counts
fumpy = pd.DataFrame(columns=['president'] + gents)
# Iterate over each president in df1
for president in df1['president'].unique():
# Initialize a dictionary to store the counts for each president
president_counts = {'president': president}
# Filter df1 for speeches by the current president
president_df = df1[df1['president'] == president]
# Iterate over each word in gents
for word in gents:
# Count the occurrences of the word in the current president's speeches
word_count = sum(president_df['transcript'].str.count(word))
# Add the count to the dictionary
president_counts[word] = word_count
# Append the counts for the current president to fumpy
fumpy = fumpy.append(president_counts, ignore_index=True)
# Set the 'president' column as the index
fumpy.set_index('president', inplace=True)
# Calculate the sum of each column and sort the columns by the sum in descending order
fumpy = fumpy.loc[:, fumpy.sum().sort_values(ascending=False).index]
# Print the DataFrame fumpy
print(fumpy)
president first_speech last_speech
0 Franklin D. Roosevelt 1939-09-03 1945-03-01
1 Harry S. Truman 1945-04-16 1945-08-09
Japan Germany Europe Japanese Hitler Poland Army Navy \
president
Franklin D. Roosevelt 149 107 92 86 55 38 51 49
Harry S. Truman 19 23 15 10 2 17 3 0
Jews World War II
president
Franklin D. Roosevelt 0 0
Harry S. Truman 0 0
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2261974942.py:30: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
bummy.drop('index', axis=1, inplace=True)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2261974942.py:84: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
fumpy = fumpy.append(president_counts, ignore_index=True)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2261974942.py:84: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
fumpy = fumpy.append(president_counts, ignore_index=True)
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from matplotlib.colors import LinearSegmentedColormap
from matplotlib.patches import Rectangle
from matplotlib.patches import Patch
# Fill missing values with zeros
fumpy_filled = fumpy.fillna(0)
# Convert fumpy to numeric type
fumpy_numeric = fumpy_filled.apply(pd.to_numeric)
# Set the size of the heatmap
plt.figure(figsize=(15, 7)) # You can adjust the size as needed
# Create the transposed DataFrame
fumpy_transposed = fumpy_numeric.T
# Create the heatmap without annotations first
sns.heatmap(fumpy_transposed, annot=False, cmap='Blues')
# Annotate each cell with the corresponding value
for i in range(len(fumpy_transposed.index)):
for j in range(len(fumpy_transposed.columns)):
plt.text(j + 0.5, i + 0.5, fumpy_transposed.iloc[i, j], ha='center', va='center', color='red', fontsize=17)
# Set custom x-axis tick colors
x_labels = fumpy_transposed.columns
x_colors = ['red' if label == 'Franklin D. Roosevelt' else 'blue' for label in x_labels]
# Map the start and end dates for each president
date_mapping = {
row['president']: (row['first_speech'].strftime('%Y-%m-%d'), row['last_speech'].strftime('%Y-%m-%d'))
for _, row in president_dates.iterrows()
}
# Use plt.text to position the x-tick labels manually below the heatmap
y_pos_below = 10.5 # Adjust as needed to position labels below the heatmap
for idx, (label, color) in enumerate(zip(x_labels, x_colors)):
plt.text(idx + 0.5, y_pos_below, label, ha='center', va='center', fontsize=20, color=color)
start_date, end_date = date_mapping[label]
plt.text(idx + 0.5, y_pos_below + 0.8, f'{start_date} - {end_date}', ha='center', va='center', fontsize=15, color='black')
legend_elements = [
Patch(facecolor='blue', edgecolor='blue', label='Democrat'),
Patch(facecolor='red', edgecolor='red', label='Republican')
]
plt.legend(handles=legend_elements,
loc='lower left',
bbox_to_anchor=(-0.3, -0.2), # Coordinates for 'bbox_to_anchor' are relative to the axes
title="Fontcolor of Presidents",
fontsize='small', # Adjust text size
title_fontsize='medium', # Adjust title font size
frameon=True, # Toggle the frame
shadow=True) # Add shadow for better visibility
plt.xticks(ticks=[], labels=[]) # Remove default xticks to avoid overlap
# Set y-axis tick labels (word list)
plt.yticks(ticks=range(len(fumpy_transposed.index)), labels=fumpy_transposed.index, fontsize=20)
plt.xlabel('President', fontsize=20, labelpad=65)
plt.ylabel('Entities', fontsize=20)
plt.title(f'World War II Entity Frequency by Presidents \n {startyear} - {endyear} \n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.2, "The heatmap displays the frequency of specific entities mentioned in President Speeches during the signifiacnt years of World War II, with annotations indicating the start and end dates of of presidents in office during that time period, which started with Invasion of Poland by Nazi Germany that marked the start of World War II and ended with the US President Harry S. Truman announcement of Japan's surrender. Red labels highlight Republican Presidents and Blue higlights Depmocraric Presidents.", wrap=True, horizontalalignment='center', fontsize=15)
plt.show()
# Initialize a StandardScaler
scaler = StandardScaler()
# Scale the data
fumpy_standardized = pd.DataFrame(scaler.fit_transform(fumpy_transposed), columns=fumpy_transposed.columns, index=fumpy_transposed.index)
# Set the size of the heatmap
plt.figure(figsize=(15, 7)) # You can adjust the size as needed
# Create the transposed DataFrame
fumpy_transposed = fumpy_numeric.T
# Create the heatmap without annotations first
sns.heatmap(fumpy_standardized, annot=False, cmap='Blues')
# Annotate each cell with the corresponding value
for i in range(len(fumpy_transposed.index)):
for j in range(len(fumpy_transposed.columns)):
plt.text(j + 0.5, i + 0.5, fumpy_transposed.iloc[i, j], ha='center', va='center', color='red', fontsize=17)
# Set custom x-axis tick colors
x_labels = fumpy_transposed.columns
x_colors = ['red' if label == 'Franklin D. Roosevelt' else 'blue' for label in x_labels]
# Map the start and end dates for each president
date_mapping = {
row['president']: (row['first_speech'].strftime('%Y-%m-%d'), row['last_speech'].strftime('%Y-%m-%d'))
for _, row in president_dates.iterrows()
}
# Use plt.text to position the x-tick labels manually below the heatmap
y_pos_below = 10.5 # Adjust as needed to position labels below the heatmap
for idx, (label, color) in enumerate(zip(x_labels, x_colors)):
plt.text(idx + 0.5, y_pos_below, label, ha='center', va='center', fontsize=20, color=color)
start_date, end_date = date_mapping[label]
plt.text(idx + 0.5, y_pos_below + 0.8, f'{start_date} - {end_date}', ha='center', va='center', fontsize=15, color='black')
legend_elements = [
Patch(facecolor='blue', edgecolor='blue', label='Democrat'),
Patch(facecolor='red', edgecolor='red', label='Republican')
]
plt.legend(handles=legend_elements,
loc='lower left',
bbox_to_anchor=(-0.3, -0.2), # Coordinates for 'bbox_to_anchor' are relative to the axes
title="Fontcolor of Presidents",
fontsize='small', # Adjust text size
title_fontsize='medium', # Adjust title font size
frameon=True, # Toggle the frame
shadow=True) # Add shadow for better visibility
plt.xticks(ticks=[], labels=[]) # Remove default xticks to avoid overlap
# Set y-axis tick labels (word list)
plt.yticks(ticks=range(len(fumpy_transposed.index)), labels=fumpy_transposed.index, fontsize=20)
plt.xlabel('President', fontsize=20, labelpad=65)
plt.ylabel('Entities', fontsize=20)
plt.title(f'World War II Entity Frequency by Presidents \n {startyear} - {endyear} Standardized \n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.2, "The heatmap displays the Standardized frequency of specific entities mentioned in President Speeches during the signifiacnt years of World War II, with annotations indicating the start and end dates of of presidents in office during that time period, which started with Invasion of Poland by Nazi Germany that marked the start of World War II and ended with the US President Harry S. Truman announcement of Japan's surrender. Red labels highlight Republican Presidents and Blue higlights Depmocraric Presidents.", wrap=True, horizontalalignment='center', fontsize=15)
plt.show()
# Initialize a MinMaxScaler
scaler = MinMaxScaler()
# Scale the data
fumpy_scaled = pd.DataFrame(scaler.fit_transform(fumpy_transposed), columns=fumpy_transposed.columns, index=fumpy_transposed.index)
# Set the size of the heatmap
plt.figure(figsize=(15, 7)) # You can adjust the size as needed
# Create the transposed DataFrame
fumpy_transposed = fumpy_numeric.T
# Create the heatmap without annotations first
sns.heatmap(fumpy_scaled, annot=False, cmap='Blues')
# Annotate each cell with the corresponding value
for i in range(len(fumpy_transposed.index)):
for j in range(len(fumpy_transposed.columns)):
plt.text(j + 0.5, i + 0.5, fumpy_transposed.iloc[i, j], ha='center', va='center', color='red', fontsize=17)
# Set custom x-axis tick colors
x_labels = fumpy_transposed.columns
x_colors = ['red' if label == 'Franklin D. Roosevelt' else 'blue' for label in x_labels]
# Map the start and end dates for each president
date_mapping = {
row['president']: (row['first_speech'].strftime('%Y-%m-%d'), row['last_speech'].strftime('%Y-%m-%d'))
for _, row in president_dates.iterrows()
}
# Use plt.text to position the x-tick labels manually below the heatmap
y_pos_below = 10.5 # Adjust as needed to position labels below the heatmap
for idx, (label, color) in enumerate(zip(x_labels, x_colors)):
plt.text(idx + 0.5, y_pos_below, label, ha='center', va='center', fontsize=20, color=color)
start_date, end_date = date_mapping[label]
plt.text(idx + 0.5, y_pos_below + 0.8, f'{start_date} - {end_date}', ha='center', va='center', fontsize=15, color='black')
legend_elements = [
Patch(facecolor='blue', edgecolor='blue', label='Democrat'),
Patch(facecolor='red', edgecolor='red', label='Republican')
]
plt.legend(handles=legend_elements,
loc='lower left',
bbox_to_anchor=(-0.3, -0.2), # Coordinates for 'bbox_to_anchor' are relative to the axes
title="Fontcolor of Presidents",
fontsize='small', # Adjust text size
title_fontsize='medium', # Adjust title font size
frameon=True, # Toggle the frame
shadow=True) # Add shadow for better visibility
plt.xticks(ticks=[], labels=[]) # Remove default xticks to avoid overlap
# Set y-axis tick labels (word list)
plt.yticks(ticks=range(len(fumpy_transposed.index)), labels=fumpy_transposed.index, fontsize=20)
plt.xlabel('President', fontsize=20, labelpad=65)
plt.ylabel('Entities', fontsize=20)
plt.title(f'World War II Entity Frequency by Presidents \n {startyear} - {endyear} MinMax Scaled \n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.2, "The heatmap displays the MinMax Scaled frequency of specific entities mentioned in President Speeches during the signifiacnt years of World War II, with annotations indicating the start and end dates of of presidents in office during that time period, which started with Invasion of Poland by Nazi Germany that marked the start of World War II and ended with the US President Harry S. Truman announcement of Japan's surrender. Red labels highlight Republican Presidents and Blue higlights Depmocraric Presidents.", wrap=True, horizontalalignment='center', fontsize=15)
plt.show()
Case Study 2¶
samp_sorted= pd.read_csv('outputfinal_filename.csv')
startyear=1947
endyear=1991
startDate=19470312 # The Cold War began with the announcement of the Truman Doctrine in 1947, started a gradual winding down with the Sino-Soviet split between the Soviets and the People's Republic of China in 1961, and ended with the collapse of the Soviet Union in 1991.
endDate=19911225 # Gorbachev resigned on 25 December 1991 and what was left of the Soviet parliament voted to end itself. the Soviet Union itself dissolved into its component republics. Sign of the end of the cold war.
gents = [
"the Soviet Union", "Russia", "Soviet",
"Communist", "Soviets", "Communists", 'Army', 'Navy'
]
bummy = samp_sorted.loc[(samp_sorted['Year'] >= startyear) & (samp_sorted['Year'] <= endyear)]
bummy.reset_index(inplace=True)
bummy.drop('index', axis=1, inplace=True)
df1 = pd.DataFrame(columns=['doc_name', 'date', 'transcript', 'president', 'title', 'Year'])
prev=startDate
for i in range(len(bummy)):
date_string = bummy.loc[bummy.index[i], 'date'] # Accessing the 'date' column for each row
cleaned_string = date_string.replace('-', '')
# Convert string to number
if 'T' in cleaned_string:
cleaned_string=cleaned_string.split('T')[0]
result = int(cleaned_string)
if result<9999:
original_str = str(prev)
bluuuh = str(result)
# Cut off the last four characters
last_four = original_str[-4:]
# Combine the remaining string with the new string
res = bluuuh + last_four
result =int(res)
prev=result
fumm=str(result)
summmy=fumm[:4] + '-' + fumm[4:6] + '-' + fumm[6:]
bummy.loc[bummy.index[i], 'date'] = summmy
if startDate <= result <= endDate:
# Assuming df1 is the DataFrame you want to add a row to, and df2 is the DataFrame from which you want to add the row
row_to_add = bummy.loc[i] # Select the row you want to add from df2
df1.loc[len(df1)] = row_to_add.values
# Ensure the 'date' column is in datetime format
#df1['date'] = pd.to_datetime(df1['date'])
# Ensure the 'date' column is in datetime format
df1['date'] = pd.to_datetime(df1['date'])
# Create a DataFrame with each president's first and last speech date
president_dates = df1.groupby('president')['date'].agg(['min', 'max']).reset_index()
president_dates.columns = ['president', 'first_speech', 'last_speech']
# Print the new DataFrame
print(president_dates)
# Create an empty DataFrame to store the counts
fumpy = pd.DataFrame(columns=['president'] + gents)
# Iterate over each president in df1
for president in df1['president'].unique():
# Initialize a dictionary to store the counts for each president
president_counts = {'president': president}
# Filter df1 for speeches by the current president
president_df = df1[df1['president'] == president]
# Iterate over each word in gents
for word in gents:
# Count the occurrences of the word in the current president's speeches
word_count = sum(president_df['transcript'].str.count(word))
# Add the count to the dictionary
president_counts[word] = word_count
# Append the counts for the current president to fumpy
fumpy = fumpy.append(president_counts, ignore_index=True)
# Set the 'president' column as the index
fumpy.set_index('president', inplace=True)
# Calculate the sum of each column and sort the columns by the sum in descending order
fumpy = fumpy.loc[:, fumpy.sum().sort_values(ascending=False).index]
republican_presidents_df = pd.DataFrame({
"Name": [
"Dwight D. Eisenhower",
"Richard M. Nixon",
"Gerald Ford",
"Ronald Reagan",
"George H. W. Bush"
]
})
# Print the DataFrame fumpy
print(fumpy)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1593383008.py:27: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
bummy.drop('index', axis=1, inplace=True)
president first_speech last_speech
0 Dwight D. Eisenhower 1953-01-20 1961-01-17
1 George H. W. Bush 1988-08-18 1991-07-31
2 Gerald Ford 1974-08-09 1977-01-12
3 Harry S. Truman 1947-03-12 1953-01-15
4 Jimmy Carter 1976-09-23 1981-01-14
5 John F. Kennedy 1960-07-15 1963-10-26
6 Lyndon B. Johnson 1963-05-30 1969-01-14
7 Richard M. Nixon 1952-09-23 1974-08-09
8 Ronald Reagan 1964-10-27 1989-01-11
Soviet the Soviet Union Communist Communists Soviets \
president
Harry S. Truman 30 12 45 17 0
Richard M. Nixon 43 25 45 15 5
Dwight D. Eisenhower 103 34 51 13 17
John F. Kennedy 159 85 178 79 17
Lyndon B. Johnson 120 70 182 47 5
Ronald Reagan 514 182 29 4 106
Gerald Ford 21 18 3 0 0
Jimmy Carter 161 95 14 3 12
George H. W. Bush 137 71 2 0 11
Russia Army Navy
president
Harry S. Truman 7 2 1
Richard M. Nixon 0 2 0
Dwight D. Eisenhower 14 4 7
John F. Kennedy 17 12 3
Lyndon B. Johnson 30 24 9
Ronald Reagan 23 7 8
Gerald Ford 0 2 2
Jimmy Carter 15 5 8
George H. W. Bush 2 4 1
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1593383008.py:117: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. fumpy = fumpy.append(president_counts, ignore_index=True) /var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1593383008.py:117: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. fumpy = fumpy.append(president_counts, ignore_index=True) /var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1593383008.py:117: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. fumpy = fumpy.append(president_counts, ignore_index=True) /var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1593383008.py:117: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. fumpy = fumpy.append(president_counts, ignore_index=True) /var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1593383008.py:117: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. fumpy = fumpy.append(president_counts, ignore_index=True) /var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1593383008.py:117: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. fumpy = fumpy.append(president_counts, ignore_index=True) /var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1593383008.py:117: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. fumpy = fumpy.append(president_counts, ignore_index=True) /var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1593383008.py:117: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. fumpy = fumpy.append(president_counts, ignore_index=True) /var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1593383008.py:117: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. fumpy = fumpy.append(president_counts, ignore_index=True)
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from matplotlib.colors import LinearSegmentedColormap
from matplotlib.patches import Rectangle
# Fill missing values with zeros
fumpy_filled = fumpy.fillna(0)
# Convert fumpy to numeric type
fumpy_numeric = fumpy_filled.apply(pd.to_numeric)
# Set the size of the heatmap
plt.figure(figsize=(15, 7)) # You can adjust the size as needed
# Create the transposed DataFrame
fumpy_transposed = fumpy_numeric.T
# Create the heatmap without annotations first
sns.heatmap(fumpy_transposed, annot=False, cmap='Blues')
# Annotate each cell with the corresponding value
for i in range(len(fumpy_transposed.index)):
for j in range(len(fumpy_transposed.columns)):
plt.text(j + 0.5, i + 0.5, fumpy_transposed.iloc[i, j], ha='center', va='center', color='red', fontsize=17)
# Set custom x-axis tick colors
x_labels = fumpy_transposed.columns
x_colors = ['red' if label in republican_presidents_df['Name'].values else 'blue' for label in x_labels]
# Map the start and end dates for each president
date_mapping = {
row['president']: (row['first_speech'].strftime('%Y-%m-%d'), row['last_speech'].strftime('%Y-%m-%d'))
for _, row in president_dates.iterrows()
}
# Use plt.text to position the x-tick labels manually below the heatmap
y_pos_below = 9 # Adjust as needed to position labels below the heatmap
for idx, (label, color) in enumerate(zip(x_labels, x_colors)):
plt.text(idx , y_pos_below, label, ha='center', va='center', fontsize=15, color=color, rotation=40)
start_date, end_date = date_mapping[label]
plt.text(idx+ .2, y_pos_below + .2, f'{start_date} - {end_date}', ha='center', va='center', fontsize=12, color='black', rotation=40)
legend_elements = [
Patch(facecolor='blue', edgecolor='blue', label='Democrat'),
Patch(facecolor='red', edgecolor='red', label='Republican')
]
plt.legend(handles=legend_elements,
loc='lower left',
bbox_to_anchor=(-0.3, -0.2), # Coordinates for 'bbox_to_anchor' are relative to the axes
title="Fontcolor of Presidents",
fontsize='small', # Adjust text size
title_fontsize='medium', # Adjust title font size
frameon=True, # Toggle the frame
shadow=True) # Add shadow for better visibility
plt.xticks(ticks=[], labels=[]) # Remove default xticks to avoid overlap
# Set y-axis tick labels (word list)
plt.yticks(ticks=range(len(fumpy_transposed.index)), labels=fumpy_transposed.index, fontsize=20)
plt.xlabel('President', fontsize=20, labelpad=130)
plt.ylabel('Entities', fontsize=20)
plt.title(f'Cold War Entity Frequency by Presidents \n {startyear} - {endyear} \n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.3, "The heatmap displays the frequency of specific entities mentioned in President Speeches during the signifiacnt years of Cold War, with annotations indicating the start and end dates of of presidents in office during that time period starting with the announcement of the Truman Doctrine in 1947 and ends with Gorbachev resigning and what was left of the Soviet parliament voted to end itself. Red labels highlight Republican Presidents and Blue higlights Depmocraric Presidents.", wrap=True, horizontalalignment='center', fontsize=15)
plt.show()
# Initialize a StandardScaler
scaler = StandardScaler()
# Scale the data
fumpy_standardized = pd.DataFrame(scaler.fit_transform(fumpy_transposed), columns=fumpy_transposed.columns, index=fumpy_transposed.index)
# Set the size of the heatmap
plt.figure(figsize=(15, 7)) # You can adjust the size as needed
# Create the transposed DataFrame
fumpy_transposed = fumpy_numeric.T
# Create the heatmap without annotations first
sns.heatmap(fumpy_standardized, annot=False, cmap='Blues')
# Annotate each cell with the corresponding value
for i in range(len(fumpy_transposed.index)):
for j in range(len(fumpy_transposed.columns)):
plt.text(j + 0.5, i + 0.5, fumpy_transposed.iloc[i, j], ha='center', va='center', color='red', fontsize=17)
# Set custom x-axis tick colors
x_labels = fumpy_transposed.columns
x_colors = ['red' if label in republican_presidents_df['Name'].values else 'blue' for label in x_labels]
# Map the start and end dates for each president
date_mapping = {
row['president']: (row['first_speech'].strftime('%Y-%m-%d'), row['last_speech'].strftime('%Y-%m-%d'))
for _, row in president_dates.iterrows()
}
# Use plt.text to position the x-tick labels manually below the heatmap
y_pos_below = 9 # Adjust as needed to position labels below the heatmap
for idx, (label, color) in enumerate(zip(x_labels, x_colors)):
plt.text(idx , y_pos_below, label, ha='center', va='center', fontsize=15, color=color, rotation=40)
start_date, end_date = date_mapping[label]
plt.text(idx+ .2, y_pos_below + .2, f'{start_date} - {end_date}', ha='center', va='center', fontsize=12, color='black', rotation=40)
legend_elements = [
Patch(facecolor='blue', edgecolor='blue', label='Democrat'),
Patch(facecolor='red', edgecolor='red', label='Republican')
]
plt.legend(handles=legend_elements,
loc='lower left',
bbox_to_anchor=(-0.3, -0.2), # Coordinates for 'bbox_to_anchor' are relative to the axes
title="Fontcolor of Presidents",
fontsize='small', # Adjust text size
title_fontsize='medium', # Adjust title font size
frameon=True, # Toggle the frame
shadow=True) # Add shadow for better visibility
plt.xticks(ticks=[], labels=[]) # Remove default xticks to avoid overlap
# Set y-axis tick labels (word list)
plt.yticks(ticks=range(len(fumpy_transposed.index)), labels=fumpy_transposed.index, fontsize=20)
plt.xlabel('President', fontsize=20, labelpad=130)
plt.ylabel('Entities', fontsize=20)
plt.title(f'Cold War Entity Frequency by Presidents \n {startyear} - {endyear} Standardized \n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.3, "The heatmap displays the Standardized frequency of specific entities mentioned in President Speeches during the signifiacnt years of Cold War, with annotations indicating the start and end dates of of presidents in office during that time period starting with the announcement of the Truman Doctrine in 1947 and ends with Gorbachev resigning and what was left of the Soviet parliament voted to end itself. Red labels highlight Republican Presidents and Blue higlights Depmocraric Presidents.", wrap=True, horizontalalignment='center', fontsize=15)
plt.show()
# Initialize a MinMaxScaler
scaler = MinMaxScaler()
# Scale the data
fumpy_scaled = pd.DataFrame(scaler.fit_transform(fumpy_transposed), columns=fumpy_transposed.columns, index=fumpy_transposed.index)
# Set the size of the heatmap
plt.figure(figsize=(15, 7)) # You can adjust the size as needed
# Create the transposed DataFrame
fumpy_transposed = fumpy_numeric.T
# Create the heatmap without annotations first
sns.heatmap(fumpy_scaled, annot=False, cmap='Blues')
# Annotate each cell with the corresponding value
for i in range(len(fumpy_transposed.index)):
for j in range(len(fumpy_transposed.columns)):
plt.text(j + 0.5, i + 0.5, fumpy_transposed.iloc[i, j], ha='center', va='center', color='red', fontsize=17)
# Set custom x-axis tick colors
x_labels = fumpy_transposed.columns
x_colors = ['red' if label in republican_presidents_df['Name'].values else 'blue' for label in x_labels]
# Map the start and end dates for each president
date_mapping = {
row['president']: (row['first_speech'].strftime('%Y-%m-%d'), row['last_speech'].strftime('%Y-%m-%d'))
for _, row in president_dates.iterrows()
}
# Use plt.text to position the x-tick labels manually below the heatmap
y_pos_below = 9 # Adjust as needed to position labels below the heatmap
for idx, (label, color) in enumerate(zip(x_labels, x_colors)):
plt.text(idx , y_pos_below, label, ha='center', va='center', fontsize=15, color=color, rotation=40)
start_date, end_date = date_mapping[label]
plt.text(idx+ .2, y_pos_below + .2, f'{start_date} - {end_date}', ha='center', va='center', fontsize=12, color='black', rotation=40)
legend_elements = [
Patch(facecolor='blue', edgecolor='blue', label='Democrat'),
Patch(facecolor='red', edgecolor='red', label='Republican')
]
plt.legend(handles=legend_elements,
loc='lower left',
bbox_to_anchor=(-0.3, -0.2), # Coordinates for 'bbox_to_anchor' are relative to the axes
title="Fontcolor of Presidents",
fontsize='small', # Adjust text size
title_fontsize='medium', # Adjust title font size
frameon=True, # Toggle the frame
shadow=True) # Add shadow for better visibility
plt.xticks(ticks=[], labels=[]) # Remove default xticks to avoid overlap
# Set y-axis tick labels (word list)
plt.yticks(ticks=range(len(fumpy_transposed.index)), labels=fumpy_transposed.index, fontsize=20)
plt.xlabel('President', fontsize=20, labelpad=130)
plt.ylabel('Entities', fontsize=20)
plt.title(f'Cold War Entity Frequency by Presidents \n {startyear} - {endyear} MinMax Scaled \n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.3, "The heatmap displays the MinMax Scaled frequency of specific entities mentioned in President Speeches during the signifiacnt years of Cold War, with annotations indicating the start and end dates of of presidents in office during that time period starting with the announcement of the Truman Doctrine in 1947 and ends with Gorbachev resigning and what was left of the Soviet parliament voted to end itself. Red labels highlight Republican Presidents and Blue higlights Depmocraric Presidents.", wrap=True, horizontalalignment='center', fontsize=15)
plt.show()
samp_sorted= pd.read_csv('outputfinal_filename.csv')
startyear=1949
endyear=2023
startDate=19490816 #1949, August 16. Navy establishes Middle East Force.
endDate=20230222 # Now, ealriest speach in data
gents = [
"Iraq", "the Middle East", "Afghanistan", "Iran",
"Iraqi", "Taliban", "Afghan", "al Qaeda",
"Arab", "Saddam Hussein", "Saddam Hussein's",
"Iraqis", "Somalia", "Muslim", "Al Qaeda",
"Osama bin Laden", "Afghans", "Assad", "Lebanon", "Army", "Navy", 'Nato'
]
bummy = samp_sorted.loc[(samp_sorted['Year'] >= startyear)]
bummy.reset_index(inplace=True)
bummy.drop('index', axis=1, inplace=True)
df1 = pd.DataFrame(columns=['doc_name', 'date', 'transcript', 'president', 'title', 'Year'])
prev=startDate
for i in range(len(bummy)):
date_string = bummy.loc[bummy.index[i], 'date'] # Accessing the 'date' column for each row
cleaned_string = date_string.replace('-', '')
# Convert string to number
if 'T' in cleaned_string:
cleaned_string=cleaned_string.split('T')[0]
result = int(cleaned_string)
if result<9999:
original_str = str(prev)
bluuuh = str(result)
# Cut off the last four characters
last_four = original_str[-4:]
# Combine the remaining string with the new string
res = bluuuh + last_four
result =int(res)
prev=result
fumm=str(result)
summmy=fumm[:4] + '-' + fumm[4:6] + '-' + fumm[6:]
bummy.loc[bummy.index[i], 'date'] = summmy
if startDate <= result <= endDate:
# Assuming df1 is the DataFrame you want to add a row to, and df2 is the DataFrame from which you want to add the row
row_to_add = bummy.loc[i] # Select the row you want to add from df2
df1.loc[len(df1)] = row_to_add.values
# Ensure the 'date' column is in datetime format
df1['date'] = pd.to_datetime(df1['date'])
# Create a DataFrame with each president's first and last speech date
president_dates = df1.groupby('president')['date'].agg(['min', 'max']).reset_index()
president_dates.columns = ['president', 'first_speech', 'last_speech']
# Print the new DataFrame
print(president_dates)
# Create an empty DataFrame to store the counts
fumpy = pd.DataFrame(columns=['president'] + gents)
# Iterate over each president in df1
for president in df1['president'].unique():
# Initialize a dictionary to store the counts for each president
president_counts = {'president': president}
# Filter df1 for speeches by the current president
president_df = df1[df1['president'] == president]
# Iterate over each word in gents
for word in gents:
# Count the occurrences of the word in the current president's speeches
word_count = sum(president_df['transcript'].str.count(word))
# Add the count to the dictionary
president_counts[word] = word_count
# Append the counts for the current president to fumpy
fumpy = fumpy.append(president_counts, ignore_index=True)
# Set the 'president' column as the index
fumpy.set_index('president', inplace=True)
# Calculate the sum of each column and sort the columns by the sum in descending order
fumpy = fumpy.loc[:, fumpy.sum().sort_values(ascending=False).index]
republican_presidents_df = pd.DataFrame({
"Name": [
"Donald Trump",
"Dwight D. Eisenhower",
"George H. W. Bush",
"George W. Bush",
"Gerald Ford",
"Richard M. Nixon",
"Ronald Reagan"
]
})
# Print the DataFrame fumpy
print(fumpy)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2454821912.py:31: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
bummy.drop('index', axis=1, inplace=True)
president first_speech last_speech 0 Barack Obama 2008-08-28 2017-01-10 1 Bill Clinton 1993-01-20 2001-01-18 2 Donald Trump 2017-01-20 2021-01-20 3 Dwight D. Eisenhower 1953-01-20 1961-01-17 4 George H. W. Bush 1988-08-18 1993-01-05 5 George W. Bush 2001-01-20 2009-01-15 6 Gerald Ford 1974-08-09 1977-01-12 7 Harry S. Truman 1950-01-04 1953-01-15 8 Jimmy Carter 1976-09-23 1981-01-14 9 Joe Biden 2021-01-20 2023-02-22 10 John F. Kennedy 1960-07-15 1963-10-26 11 Lyndon B. Johnson 1963-05-30 1969-01-14 12 Richard M. Nixon 1952-09-23 1974-08-09 13 Ronald Reagan 1964-10-27 1989-01-11
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2454821912.py:111: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. fumpy = fumpy.append(president_counts, ignore_index=True) /var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2454821912.py:111: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. fumpy = fumpy.append(president_counts, ignore_index=True) /var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2454821912.py:111: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. fumpy = fumpy.append(president_counts, ignore_index=True) /var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2454821912.py:111: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. fumpy = fumpy.append(president_counts, ignore_index=True) /var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2454821912.py:111: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. fumpy = fumpy.append(president_counts, ignore_index=True) /var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2454821912.py:111: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. fumpy = fumpy.append(president_counts, ignore_index=True) /var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2454821912.py:111: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. fumpy = fumpy.append(president_counts, ignore_index=True) /var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2454821912.py:111: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. fumpy = fumpy.append(president_counts, ignore_index=True) /var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2454821912.py:111: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. fumpy = fumpy.append(president_counts, ignore_index=True) /var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2454821912.py:111: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. fumpy = fumpy.append(president_counts, ignore_index=True) /var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2454821912.py:111: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. fumpy = fumpy.append(president_counts, ignore_index=True) /var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2454821912.py:111: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. fumpy = fumpy.append(president_counts, ignore_index=True) /var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2454821912.py:111: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. fumpy = fumpy.append(president_counts, ignore_index=True) /var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2454821912.py:111: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. fumpy = fumpy.append(president_counts, ignore_index=True)
Iraq Afghan Afghanistan the Middle East Iran Iraqi Arab \
president
Harry S. Truman 0 0 0 0 3 0 0
Richard M. Nixon 0 0 0 15 1 0 5
Dwight D. Eisenhower 3 0 0 34 5 0 6
John F. Kennedy 1 0 0 16 1 1 1
Lyndon B. Johnson 0 1 1 17 2 0 11
Ronald Reagan 6 57 52 51 38 0 27
Gerald Ford 0 0 0 13 0 0 2
Jimmy Carter 6 22 22 45 65 0 45
George H. W. Bush 115 3 3 23 6 23 42
Bill Clinton 12 0 0 26 3 1 5
George W. Bush 540 94 84 74 46 218 24
Barack Obama 179 188 121 30 71 44 25
Donald Trump 19 18 15 25 103 1 17
Joe Biden 10 228 159 3 4 0 4
Lebanon Army al Qaeda ... Saddam Hussein Iraqis Somalia \
president ...
Harry S. Truman 0 2 0 ... 0 0 0
Richard M. Nixon 0 2 0 ... 0 0 0
Dwight D. Eisenhower 2 4 0 ... 0 0 0
John F. Kennedy 2 12 0 ... 0 0 0
Lyndon B. Johnson 2 24 0 ... 0 0 0
Ronald Reagan 134 7 0 ... 0 0 0
Gerald Ford 0 2 0 ... 0 0 0
Jimmy Carter 1 5 0 ... 0 0 0
George H. W. Bush 3 9 0 ... 26 2 29
Bill Clinton 1 13 0 ... 8 0 34
George W. Bush 11 24 24 ... 55 61 1
Barack Obama 2 10 88 ... 2 13 4
Donald Trump 2 24 1 ... 0 0 2
Joe Biden 0 3 12 ... 0 0 3
Navy Al Qaeda Assad Osama bin Laden Afghans Nato \
president
Harry S. Truman 1 0 0 0 0 0
Richard M. Nixon 0 0 0 0 0 0
Dwight D. Eisenhower 7 0 0 0 0 0
John F. Kennedy 3 0 0 0 0 0
Lyndon B. Johnson 9 0 0 0 0 0
Ronald Reagan 8 0 0 0 0 0
Gerald Ford 2 0 0 0 0 0
Jimmy Carter 8 0 0 0 0 0
George H. W. Bush 3 0 0 0 0 0
Bill Clinton 2 0 0 1 0 0
George W. Bush 6 41 0 3 2 0
Barack Obama 3 4 31 17 9 14
Donald Trump 10 1 3 1 0 0
Joe Biden 0 2 0 9 19 0
Saddam Hussein's
president
Harry S. Truman 0
Richard M. Nixon 0
Dwight D. Eisenhower 0
John F. Kennedy 0
Lyndon B. Johnson 0
Ronald Reagan 0
Gerald Ford 0
Jimmy Carter 0
George H. W. Bush 6
Bill Clinton 0
George W. Bush 7
Barack Obama 0
Donald Trump 0
Joe Biden 0
[14 rows x 22 columns]
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from matplotlib.colors import LinearSegmentedColormap
from matplotlib.patches import Rectangle
# Fill missing values with zeros
fumpy_filled = fumpy.fillna(0)
# Convert fumpy to numeric type
fumpy_numeric = fumpy_filled.apply(pd.to_numeric)
# Set the size of the heatmap
plt.figure(figsize=(18, 10)) # You can adjust the size as needed
# Create the transposed DataFrame
fumpy_transposed = fumpy_numeric.T
# Create the heatmap without annotations first
sns.heatmap(fumpy_transposed, annot=False, cmap='Blues')
# Annotate each cell with the corresponding value
for i in range(len(fumpy_transposed.index)):
for j in range(len(fumpy_transposed.columns)):
plt.text(j + 0.5, i + 0.5, fumpy_transposed.iloc[i, j], ha='center', va='center', color='red', fontsize=17)
# Set custom x-axis tick colors
x_labels = fumpy_transposed.columns
x_colors = ['red' if label in republican_presidents_df['Name'].values else 'blue' for label in x_labels]
# Map the start and end dates for each president
date_mapping = {
row['president']: (row['first_speech'].strftime('%Y-%m-%d'), row['last_speech'].strftime('%Y-%m-%d'))
for _, row in president_dates.iterrows()
}
# Use plt.text to position the x-tick labels manually below the heatmap
y_pos_below = 25 # Adjust as needed to position labels below the heatmap
for idx, (label, color) in enumerate(zip(x_labels, x_colors)):
plt.text(idx-0.0, y_pos_below -0.5, label, ha='center', va='center', fontsize=16, color=color, rotation=40)
start_date, end_date = date_mapping[label]
plt.text(idx+0.1, y_pos_below -0.05, f'{start_date} - {end_date}', ha='center', va='center', fontsize=12, color='black', rotation=40)
legend_elements = [
Patch(facecolor='blue', edgecolor='blue', label='Democrat'),
Patch(facecolor='red', edgecolor='red', label='Republican')
]
plt.legend(handles=legend_elements,
loc='lower left',
bbox_to_anchor=(-0.3, -0.2), # Coordinates for 'bbox_to_anchor' are relative to the axes
title="Fontcolor of Presidents",
fontsize='small', # Adjust text size
title_fontsize='medium', # Adjust title font size
frameon=True, # Toggle the frame
shadow=True) # Add shadow for better visibility
plt.xticks(ticks=[], labels=[]) # Remove default xticks to avoid overlap
# Set y-axis tick labels (word list)
plt.yticks(ticks=range(len(fumpy_transposed.index)), labels=fumpy_transposed.index, fontsize=20)
plt.xlabel('President', fontsize=20, labelpad=135)
plt.ylabel('Entities', fontsize=15)
plt.title(f'Middle East Entity Frequency by Presidents \n {startyear} - {endyear} \n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.15, "The heatmap displays the frequency of specific entities mentioned in President Speeches during the signifiacnt years of Middle East involvement, with annotations indicating the start and end dates of of presidents in office during that time period starts with the Navy establishing Middle East Force for first time and this period is still going on to this day so ends with the earliest speech in data. Red labels highlight Republican Presidents and Blue higlights Depmocraric Presidents.", wrap=True, horizontalalignment='center', fontsize=15)
plt.show()
# Initialize a StandardScaler
scaler = StandardScaler()
# Scale the data
fumpy_standardized = pd.DataFrame(scaler.fit_transform(fumpy_transposed), columns=fumpy_transposed.columns, index=fumpy_transposed.index)
# Set the size of the heatmap
plt.figure(figsize=(18, 10)) # You can adjust the size as needed
# Create the transposed DataFrame
fumpy_transposed = fumpy_numeric.T
# Create the heatmap without annotations first
sns.heatmap(fumpy_standardized, annot=False, cmap='Blues')
# Annotate each cell with the corresponding value
for i in range(len(fumpy_transposed.index)):
for j in range(len(fumpy_transposed.columns)):
plt.text(j + 0.5, i + 0.5, fumpy_transposed.iloc[i, j], ha='center', va='center', color='red', fontsize=17)
# Set custom x-axis tick colors
x_labels = fumpy_transposed.columns
x_colors = ['red' if label in republican_presidents_df['Name'].values else 'blue' for label in x_labels]
# Map the start and end dates for each president
date_mapping = {
row['president']: (row['first_speech'].strftime('%Y-%m-%d'), row['last_speech'].strftime('%Y-%m-%d'))
for _, row in president_dates.iterrows()
}
# Use plt.text to position the x-tick labels manually below the heatmap
y_pos_below = 25 # Adjust as needed to position labels below the heatmap
for idx, (label, color) in enumerate(zip(x_labels, x_colors)):
plt.text(idx-0.0, y_pos_below -0.5, label, ha='center', va='center', fontsize=16, color=color, rotation=40)
start_date, end_date = date_mapping[label]
plt.text(idx+0.1, y_pos_below -0.05, f'{start_date} - {end_date}', ha='center', va='center', fontsize=12, color='black', rotation=40)
legend_elements = [
Patch(facecolor='blue', edgecolor='blue', label='Democrat'),
Patch(facecolor='red', edgecolor='red', label='Republican')
]
plt.legend(handles=legend_elements,
loc='lower left',
bbox_to_anchor=(-0.3, -0.2), # Coordinates for 'bbox_to_anchor' are relative to the axes
title="Fontcolor of Presidents",
fontsize='small', # Adjust text size
title_fontsize='medium', # Adjust title font size
frameon=True, # Toggle the frame
shadow=True) # Add shadow for better visibility
plt.xticks(ticks=[], labels=[]) # Remove default xticks to avoid overlap
# Set y-axis tick labels (word list)
plt.yticks(ticks=range(len(fumpy_transposed.index)), labels=fumpy_transposed.index, fontsize=20)
plt.xlabel('President', fontsize=20, labelpad=135)
plt.ylabel('Entities', fontsize=15)
plt.title(f'Middle East Entity Frequency by Presidents \n {startyear} - {endyear} Standardized\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.15, "The heatmap displays the Standardized frequency of specific entities mentioned in President Speeches during the signifiacnt years of Middle East involvement, with annotations indicating the start and end dates of of presidents in office during that time period starts with the Navy establishing Middle East Force for first time and this period is still going on to this day so ends with the earliest speech in data. Red labels highlight Republican Presidents and Blue higlights Depmocraric Presidents.", wrap=True, horizontalalignment='center', fontsize=15)
plt.show()
# Initialize a MinMaxScaler
scaler = MinMaxScaler()
# Scale the data
fumpy_scaled = pd.DataFrame(scaler.fit_transform(fumpy_transposed), columns=fumpy_transposed.columns, index=fumpy_transposed.index)
# Set the size of the heatmap
plt.figure(figsize=(18, 10)) # You can adjust the size as needed
# Create the transposed DataFrame
fumpy_transposed = fumpy_numeric.T
# Create the heatmap without annotations first
sns.heatmap(fumpy_scaled, annot=False, cmap='Blues')
# Annotate each cell with the corresponding value
for i in range(len(fumpy_transposed.index)):
for j in range(len(fumpy_transposed.columns)):
plt.text(j + 0.5, i + 0.5, fumpy_transposed.iloc[i, j], ha='center', va='center', color='red', fontsize=17)
# Set custom x-axis tick colors
x_labels = fumpy_transposed.columns
x_colors = ['red' if label in republican_presidents_df['Name'].values else 'blue' for label in x_labels]
# Map the start and end dates for each president
date_mapping = {
row['president']: (row['first_speech'].strftime('%Y-%m-%d'), row['last_speech'].strftime('%Y-%m-%d'))
for _, row in president_dates.iterrows()
}
# Use plt.text to position the x-tick labels manually below the heatmap
y_pos_below = 25 # Adjust as needed to position labels below the heatmap
for idx, (label, color) in enumerate(zip(x_labels, x_colors)):
plt.text(idx-0.0, y_pos_below -0.5, label, ha='center', va='center', fontsize=16, color=color, rotation=40)
start_date, end_date = date_mapping[label]
plt.text(idx+0.1, y_pos_below -0.05, f'{start_date} - {end_date}', ha='center', va='center', fontsize=12, color='black', rotation=40)
legend_elements = [
Patch(facecolor='blue', edgecolor='blue', label='Democrat'),
Patch(facecolor='red', edgecolor='red', label='Republican')
]
plt.legend(handles=legend_elements,
loc='lower left',
bbox_to_anchor=(-0.3, -0.2), # Coordinates for 'bbox_to_anchor' are relative to the axes
title="Fontcolor of Presidents",
fontsize='small', # Adjust text size
title_fontsize='medium', # Adjust title font size
frameon=True, # Toggle the frame
shadow=True) # Add shadow for better visibility
plt.xticks(ticks=[], labels=[]) # Remove default xticks to avoid overlap
# Set y-axis tick labels (word list)
plt.yticks(ticks=range(len(fumpy_transposed.index)), labels=fumpy_transposed.index, fontsize=20)
plt.xlabel('President', fontsize=20, labelpad=135)
plt.ylabel('Entities', fontsize=15)
plt.title(f'Middle East Entity Frequency by Presidents \n {startyear} - {endyear} MinMax Scaled \n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.15, "The heatmap displays the MinMax Scaled frequency of specific entities mentioned in President Speeches during the signifiacnt years of Middle East involvement, with annotations indicating the start and end dates of of presidents in office during that time period starts with the Navy establishing Middle East Force for first time and this period is still going on to this day so ends with the earliest speech in data. Red labels highlight Republican Presidents and Blue higlights Depmocraric Presidents.", wrap=True, horizontalalignment='center', fontsize=15)
plt.show()
This took really long to run, but it basically looks through all the speeches and extracts the ENtites mentioned and saves them in a list¶
'''
samp_sorted= pd.read_csv('outputfinal_filename.csv')
litz=samp_sorted.loc[:, ["Year", "transcript"]]
fr=litz["Year"].drop_duplicates()
dates=[]
for i in fr:
dates.append(i)
# Create an empty DataFrame with specified columns
woah = pd.DataFrame(columns=['year', 'entity'])
# Load the spaCy model
nlp = spacy.load('en_core_web_sm')
# Function to extract entities
def extract_entities(text):
doc = nlp(text)
return [ent.text for ent in doc.ents if ent.label_ in ['GPE', 'ORG', 'PERSON', 'NORP', 'FAC', 'LOC', 'PRODUCT', 'EVENT', 'WORK_OF_ART', 'LAW', 'LANGUAGE']]
# Applying the function to each speech
all_entities = []
for i in dates:
print(i)
cur=litz.loc[litz["Year"]== i]
cur.reset_index(inplace=True)
cur.drop('index', axis=1, inplace=True)
cur_entities = []
for j in range(len(cur)):
gum= cur.loc[j,'transcript']
entities = extract_entities(gum)
cur_entities.extend(entities)
all_entities.extend(entities)
entity_counts = Counter(cur_entities)
for entity, count in entity_counts.items():
fum= f"{entity}: {count}"
woah.loc[len(woah)] = [ i, fum]
entity2=[]
problem=[]
county=[]
for i in range(len(woah)):
print(i)
if woah.loc[i, 'entity'].count(':')==1:
ayy=woah.loc[i, 'entity'].split(":")
entity2.append(ayy[0])
county.append(ayy[1])
else:
problem.append([woah.loc[i, :], i])
for i in range(len(problem)):
index_to_remove2 = woah.index[problem[i][1]]
woah = woah.drop(index_to_remove2)
woah['entity2']=entity2
woah['county']=county
woah.reset_index(inplace=True)
woah.drop('index', axis=1, inplace=True)
woah.to_csv('entities_spec.csv', index=False)
# Create a DataFrame with a single column named "enty" for the raw list of entites
df = pd.DataFrame(all_entities, columns=['enty'])
df.to_csv('entities_raw.csv', index=False)
'''
'\nsamp_sorted= pd.read_csv(\'outputfinal_filename.csv\')\n\nlitz=samp_sorted.loc[:, ["Year", "transcript"]]\nfr=litz["Year"].drop_duplicates()\ndates=[]\n\nfor i in fr:\n dates.append(i)\n\n# Create an empty DataFrame with specified columns\nwoah = pd.DataFrame(columns=[\'year\', \'entity\'])\n\n\n# Load the spaCy model\nnlp = spacy.load(\'en_core_web_sm\')\n\n\n# Function to extract entities\ndef extract_entities(text):\n doc = nlp(text)\n return [ent.text for ent in doc.ents if ent.label_ in [\'GPE\', \'ORG\', \'PERSON\', \'NORP\', \'FAC\', \'LOC\', \'PRODUCT\', \'EVENT\', \'WORK_OF_ART\', \'LAW\', \'LANGUAGE\']]\n\n# Applying the function to each speech\nall_entities = []\n\nfor i in dates:\n print(i)\n cur=litz.loc[litz["Year"]== i]\n cur.reset_index(inplace=True)\n cur.drop(\'index\', axis=1, inplace=True)\n cur_entities = []\n \n for j in range(len(cur)):\n gum= cur.loc[j,\'transcript\']\n entities = extract_entities(gum)\n cur_entities.extend(entities)\n all_entities.extend(entities)\n \n \n entity_counts = Counter(cur_entities)\n for entity, count in entity_counts.items():\n fum= f"{entity}: {count}"\n woah.loc[len(woah)] = [ i, fum]\n \nentity2=[]\nproblem=[]\ncounty=[]\nfor i in range(len(woah)):\n print(i)\n if woah.loc[i, \'entity\'].count(\':\')==1:\n ayy=woah.loc[i, \'entity\'].split(":")\n entity2.append(ayy[0])\n county.append(ayy[1])\n else:\n problem.append([woah.loc[i, :], i])\n\n\nfor i in range(len(problem)):\n index_to_remove2 = woah.index[problem[i][1]]\n woah = woah.drop(index_to_remove2)\n \nwoah[\'entity2\']=entity2\nwoah[\'county\']=county\n\n\nwoah.reset_index(inplace=True)\nwoah.drop(\'index\', axis=1, inplace=True)\nwoah.to_csv(\'entities_spec.csv\', index=False)\n \n \n# Create a DataFrame with a single column named "enty" for the raw list of entites\ndf = pd.DataFrame(all_entities, columns=[\'enty\'])\ndf.to_csv(\'entities_raw.csv\', index=False)\n'
Saving the data for later use¶
woah = pd.read_csv('entities_spec.csv')
woah
| year | entity | entity2 | county | |
|---|---|---|---|---|
| 0 | 1789 | Fellow Citizens: 1 | Fellow Citizens | 1 |
| 1 | 1789 | Senate: 1 | Senate | 1 |
| 2 | 1789 | the House of Representatives: 2 | the House of Representatives | 2 |
| 3 | 1789 | Country: 2 | Country | 2 |
| 4 | 1789 | Act: 1 | Act | 1 |
| ... | ... | ... | ... | ... |
| 44855 | 2023 | First Lady: 1 | First Lady | 1 |
| 44856 | 2023 | Russians: 1 | Russians | 1 |
| 44857 | 2023 | Allies: 2 | Allies | 2 |
| 44858 | 2023 | NATO Alliance: 1 | NATO Alliance | 1 |
| 44859 | 2023 | Article 5: 1 | Article 5 | 1 |
44860 rows × 4 columns
Now counting the number of each entity have been detected¶
df = pd.read_csv("entities_raw.csv")
# Extract the "col1" column as a list of strings
all_entities = df['enty'].tolist()
entity_counts = Counter(all_entities)
allofem=[]
for entity, count in entity_counts.items():
fum= f"{entity}: {count}"
allofem.append(fum)
# Using most_common method
ordered_counts = entity_counts.most_common()
ents=[]
for entity, count in ordered_counts:
print(f"{entity}: {count}")
en= [entity,count ]
ents.append(en)
the United States: 8285 Congress: 8163 American: 6278 America: 4351 Americans: 2724 State: 2275 States: 1998 Constitution: 1873 Government: 1583 Senate: 1572 Treasury: 1481 Washington: 1156 Europe: 1037 Mexico: 978 China: 841 Navy: 777 Army: 760 Great Britain: 679 Cuba: 618 the Soviet Union: 613 British: 603 Iraq: 590 Spain: 587 Indian: 571 France: 520 the Federal Government: 516 Indians: 506 Texas: 497 Vietnam: 473 House: 472 Russia: 467 Afghanistan: 458 Soviet: 450 Executive: 442 the House of Representatives: 434 Republican: 430 Republicans: 427 the United Nations: 407 United States: 399 Democrats: 395 Department: 387 Japan: 387 Germany: 386 The United States: 382 Asia: 380 the United States of America: 367 European: 364 the Middle East: 363 Chinese: 361 Pacific: 357 Communist: 354 Israel: 332 California: 327 Africa: 323 Territory: 323 New York: 318 Iran: 282 the White House: 280 U.S.: 277 Kennedy: 276 God: 274 Democratic: 270 Panama: 261 Medicare: 252 Atlantic: 251 Missouri: 248 NATO: 247 the Supreme Court: 245 Mississippi: 243 Nation: 240 the General Government: 235 West: 233 French: 233 Earth: 231 Berlin: 226 South: 224 Nicaragua: 224 Mexican: 223 Commission: 223 Spanish: 217 Canada: 215 Administration: 215 German: 212 Carter: 196 Social Security: 196 Cabinet: 195 Paris: 194 Iraqi: 191 Ford: 190 the District of Columbia: 189 Nebraska: 189 House of Representatives: 184 Louisiana: 181 Florida: 177 Russian: 177 Interior: 177 Soviets: 177 Communists: 175 Cuban: 174 Korea: 174 Q.: 174 Latin America: 170 Kansas: 167 Ohio: 166 South Vietnam: 165 Central America: 163 Italy: 163 Bush: 163 Isthmus: 160 Philippines: 160 Territories: 159 Lebanon: 159 Poland: 159 Japanese: 157 World War II: 157 Laughter: 156 the Postmaster-General: 154 Federal: 153 London: 149 Reagan: 149 the National Government: 147 Nixon: 147 republican: 145 Brazil: 145 New Mexico: 145 Alaska: 145 Viet-Nam: 145 Congressional: 139 Virginia: 138 Ukraine: 137 Turkey: 134 North: 133 the War Department: 128 Oregon: 128 South Carolina: 126 New Orleans: 125 Governments: 124 East: 124 Geneva: 123 Mondale: 123 Speaker: 122 England: 122 English: 121 Pennsylvania: 120 Georgia: 120 Taliban: 118 the Constitution of the United States: 116 Venezuela: 116 the Department of State: 115 Departments: 115 Eisenhower: 115 Greece: 113 Hawaii: 112 South Vietnamese: 112 Afghan: 112 Alabama: 111 South Viet-Nam: 111 North Vietnam: 111 Democrat: 110 Illinois: 109 the British Government: 108 Hanoi: 108 African: 107 Gorbachev: 106 al Qaeda: 106 Utah: 105 the Treasury Department: 104 Chile: 103 the Mexican Government: 100 Arab: 99 Clinton: 98 the Congress of the United States: 95 the Post-Office Department: 95 Southeast Asia: 94 India: 92 the Democratic Party: 92 San Francisco: 91 Chicago: 91 Trump: 91 the Bank of the United States: 90 the Republican Party: 90 Western Europe: 90 Peru: 89 Egypt: 89 Republics: 87 Jefferson: 87 Austria: 86 Moscow: 86 Massachusetts: 85 Khrushchev: 85 earth: 84 South America: 84 Negro: 84 Colombia: 83 U.N.: 83 Legislature: 82 Philadelphia: 81 the Western Hemisphere: 81 Pakistan: 81 Dole: 80 Defense: 80 Kuwait: 80 War: 79 Portugal: 79 Stanton: 79 Houses of Congress: 78 Americas: 78 Clay: 77 Providence: 76 Arkansas: 76 Court: 76 Houses: 75 Britain: 75 Philippine: 75 Saddam Hussein: 75 Iraqis: 75 the Department of Justice: 74 Truman: 74 Lincoln: 73 the Panama Canal: 73 Watergate: 73 Somalia: 73 Mediterranean: 72 Porto Rico: 72 D.C.: 72 SMITH: 72 Bosnia: 72 Arizona: 71 Medicaid: 71 Tennessee: 70 Capitol: 70 the Rio Grande: 70 Honduras: 70 Palestinians: 70 Puerto Rico: 69 the Department of Agriculture: 69 Syria: 69 the Spanish Government: 68 the Dominican Republic: 68 Cambodia: 68 Palestinian: 68 Biden: 68 North Carolina: 67 Johnson: 67 Gulf: 67 Boston: 66 Denmark: 66 Belgium: 66 the Interstate Commerce Commission: 66 South Korea: 66 ’s: 66 North Korea: 65 Detroit: 64 Republic: 64 the French Government: 64 Canadian: 64 Putin: 64 Agriculture: 63 Maine: 63 Michigan: 63 the Southern States: 63 North Vietnamese: 63 Roosevelt: 62 Eastern Europe: 62 Israelis: 62 COVID-19: 62 Baltimore: 61 Lehrer: 61 the State Department: 60 Douglas: 60 Nazi: 60 AUDIENCE: 60 Abraham Lincoln: 59 Grant: 59 Polish: 58 Israeli: 58 congress: 57 West Point: 57 Iowa: 57 Castro: 57 New Jersey: 56 Southern: 56 the Civil War: 56 Hitler: 56 KENNEDY: 56 Laos: 56 Perot: 56 Kentucky: 55 Havana: 55 CARTER: 55 APPLAUSE: 55 Guatemala: 54 Australia: 54 Russians: 54 White House: 54 REAGAN: 54 John: 53 Manila: 53 Sweden: 52 Indiana: 52 Madrid: 52 the United States Government: 52 Italian: 52 Iranian: 52 the Rocky Mountains: 51 Hungary: 51 Governors: 51 Nancy: 51 USA: 51 Representatives: 50 George Washington: 50 Christian: 50 Monroe: 50 Germans: 50 the Republic of Panama: 50 Yugoslavia: 50 Middle East: 50 ISIS: 50 Colorado: 49 FBI: 49 NIXON: 49 Selma: 49 Joe Biden: 49 Maryland: 48 fidelity: 48 the Gulf of Mexico: 48 the Chinese Government: 48 the United Kingdom: 48 Budget: 48 Vietnamese: 48 Mike: 48 Muslim: 48 the Federal Constitution: 47 Cubans: 47 Peking: 47 Nazis: 47 United Nations: 47 The Soviet Union: 47 McNamara: 47 DOLE: 47 the Pacific Ocean: 46 Grenada: 46 the Declaration of Independence: 46 Asian: 46 Saudi Arabia: 46 El Salvador: 46 Rome: 45 democratic: 45 Costa Rica: 45 Honolulu: 45 the Far East: 45 the Oval Office: 45 CLINTON: 45 Al Qaeda: 45 Revolution: 44 St. Louis: 44 Vienna: 44 West Berlin: 44 Steve: 44 Kosovo: 44 CDC: 44 Bolivia: 43 New York City: 43 Labor: 43 Pearl Harbor: 43 Indochina: 43 Muslims: 43 National: 42 South American: 42 Netherlands: 42 The Federal Government: 42 Caribbean: 42 North Viet-Nam: 42 Baghdad: 42 the Isthmus of Panama: 41 the Navy Department: 41 Chamber: 41 New Hampshire: 41 Thomas Jefferson: 41 Formosa: 41 Armed Forces: 41 Connecticut: 40 Eastern: 40 Minnesota: 40 Chilean: 40 the National Guard: 40 Filipinos: 40 The United Nations: 40 Franklin Roosevelt: 40 the Cold War: 40 Walters: 40 NAFTA: 40 TRUMP: 40 Jackson: 39 Hon: 39 Liberia: 39 the Missouri Compromise: 39 TELEGRAM: 39 Colon: 39 Obama: 39 Prussia: 38 Paraguay: 38 Norway: 38 Ecuador: 38 the Provisional Government: 38 Beirut: 38 Jewish: 38 North Africa: 38 Pentagon: 38 BUSH: 38 DUKAKIS: 38 Justice: 37 Western: 37 the West Indies: 37 the Regular Army: 37 Rhode Island: 37 Bureau: 37 Oklahoma: 37 Haiti: 37 the Joint Chiefs of Staff: 37 John F. Kennedy: 37 Ireland: 36 the Mexican Republic: 36 World War: 36 Kremlin: 36 Ukrainian: 36 NASA: 36 Islam: 36 Fellow-Citizens of the Senate: 35 Parliament: 35 Hawaiian: 35 the State of Texas: 35 Rusk: 35 Hay: 35 Libya: 35 Saddam: 35 North American: 34 Wisconsin: 34 Switzerland: 34 the Revised Statutes: 34 Jews: 34 CIA: 34 Westmoreland: 34 African American: 34 Treaty: 33 Supreme Court: 33 Board: 33 North America: 33 Jim: 33 the People's Republic of China: 33 FDA: 33 Cherokees: 32 Union: 32 the General Land Office: 32 Chambers: 32 the Persian Gulf: 32 Angola: 32 George Bush: 32 the Independence of the United States: 31 the Government of Spain: 31 New Zealand: 31 Santiago: 31 Cairo: 31 Marshall: 31 Martin Luther King: 31 Fauci: 31 Potomac: 30 the Argentine Republic: 30 Colombian: 30 Bible: 30 Congo: 30 Woodrow Wilson: 30 Colonies: 30 Latin American: 30 NEWMAN: 30 Brady: 30 Osama bin Laden: 30 Afghans: 30 Assad: 30 us: 29 King: 29 Brussels: 29 Taylor: 29 I.: 29 Allies: 29 Ronald Reagan: 29 Harry Truman: 29 Vietcong: 29 the Mississippi River: 28 Catholic: 28 the American Government: 28 The Hague: 28 Tokyo: 28 Los Angeles: 28 Allied: 28 the World War: 28 Marines: 28 OAS: 28 Birx: 28 Columbia: 27 Delaware: 27 Empire: 27 Executive Departments: 27 Mexicans: 27 San Juan: 27 West Virginia: 27 Cleveland: 27 Taft: 27 Iceland: 27 Burma: 27 Air Force: 27 the Free World: 27 Quemoy: 27 the Great Depression: 27 Hillary: 27 Commerce: 26 Assembly: 26 navy: 26 Charleston: 26 the Great Lakes: 26 Greek: 26 The United States of America: 26 Madison: 26 Presidential: 26 the Interior Department: 26 Valparaiso: 26 McKinley: 26 Lodge: 26 South Africa: 26 Palestine: 26 Indonesia: 26 Brezhnev: 26 SALT: 26 Hispanic: 26 Mitch: 26 Divine Providence: 25 Britannic Majesty: 25 Christians: 25 the National Treasury: 25 the Old World: 25 Kingdom: 25 West India: 25 Texan: 25 Negroes: 25 the Pension Bureau: 25 SEC: 25 Dominion: 25 Italians: 25 Churchill: 25 the Second World War: 25 the Armed Forces: 25 the Defense Department: 25 Matsu: 25 Saigon: 25 OPEC: 25 MONDALE: 25 Northern Ireland: 25 Holland: 24 Commissioners: 24 the Patent Office: 24 Indian Affairs: 24 the City of Mexico: 24 Atlanta: 24 Transportation: 24 the Department of Commerce: 24 Czechoslovakia: 24 Irish: 24 Winston Churchill: 24 Yalta: 24 the United Nations Charter: 24 the State of the Union: 24 Joe: 24 Taiwan: 24 Richard Nixon: 24 the Peace Corps: 24 Yemen: 24 Lebanese: 24 John McCain: 24 Imperial: 23 Bogota: 23 the Chinese Empire: 23 the War of 1812: 23 the Republic of Mexico: 23 Central American: 23 Topeka: 23 Montana: 23 The Department of Agriculture: 23 Samoan: 23 Bulgaria: 23 Kansas City: 23 the Federal Reserve Board: 23 REYNOLDS: 23 Jordan: 23 Tony: 23 Michelle: 23 these United States: 22 the National Legislature: 22 Brown: 22 Lakes: 22 Council: 22 Africans: 22 Bill: 22 US: 22 Convention: 22 the Pacific Coast: 22 Nicaraguan: 22 Salvador: 22 Dingley: 22 Theodore Roosevelt: 22 Singapore: 22 inter-American: 22 Goldwater: 22 Kissinger: 22 Syrian: 22 FEMA: 22 Nancy Pelosi: 22 Barrett: 22 Revolutionary: 21 the Republic of Colombia: 21 the States of the Union: 21 the Imperial Government: 21 Henry Clay: 21 the Republican party: 21 the United States Army: 21 the Dominion of Canada: 21 Samoa: 21 the Tariff Commission: 21 Wilson: 21 The National Government: 21 NAVY: 21 Canal: 21 Tunisia: 21 Warsaw: 21 the Security Council: 21 Jerusalem: 21 John Kennedy: 21 Arabs: 21 Gaza: 21 Newtown: 21 Xi: 21 the State of: 20 Lake Erie: 20 Morocco: 20 Confederation: 20 Majesty: 20 the State of New York: 20 Turkish: 20 colonies: 20 Federal Government: 20 the New World: 20 Baker: 20 the Government of Chile: 20 Omaha: 20 the German Government: 20 Alaskan: 20 the Bering Sea: 20 the Republic of Cuba: 20 Filipino: 20 the Department of Commerce and Labor: 20 the Near East: 20 the United Nations Security Council: 20 Camp David: 20 Capitol Hill: 20 Cyprus: 20 Islamic: 20 Border Patrol: 20 Google: 20 COVID: 20 Nations: 19 Session: 19 Constitutional: 19 Academy: 19 the Post Office Department: 19 Asiatic: 19 Andrew Jackson: 19 the Marine Corps: 19 Ryan: 19 Paredes: 19 Central and South America: 19 The Supreme Court: 19 the Department of the Interior: 19
Annapolis: 19 North and South: 19 Teheran: 19 Young: 19 Shanghai: 19 San Domingo: 19 Burlingame: 19 Ehrman: 19 Dominican: 19 the Imperial German Government: 19 Serbia: 19 Ethiopia: 19 Thailand: 19 Ghana: 19 Robert Kennedy: 19 Houston: 19 the Justice Department: 19 Bob Dole: 19 SALT II: 19 Barbara: 19 bin Laden: 19 Jill: 19 Fellow Citizens: 18 Country: 18 the Government of the United States: 18 the French Republic: 18 John Adams: 18 Time: 18 Sabine: 18 Chesapeake: 18 Buenos Ayres: 18 Rio de Janeiro: 18 the Postmaster General: 18 Brazilian: 18 Commonwealth: 18 the Federal Treasury: 18 the State of Virginia: 18 Vera Cruz: 18 Guadalupe Hidalgo: 18 Korean: 18 Domingo: 18 Kellogg: 18 Yale: 18 MacArthur: 18 Lithuania: 18 the Air Force: 18 Franklin Delano Roosevelt: 18 Welfare: 18 the World Bank: 18 the Organization of American States: 18 Alliance: 18 Gore: 18 Thieu: 18 VA: 18 AmeriCorps: 18 Rwanda: 18 Mitch McConnell: 18 Citizens: 17 Tripoli: 17 Northwest: 17 Cumberland: 17 the German Empire: 17 Scott: 17 San Diego: 17 Harris: 17 the Chilean Government: 17 The Republican Party: 17 PANAMA: 17 Marine: 17 Normandy: 17 Harry S. Truman: 17 the General Assembly: 17 Stalin: 17 the Department of Defense: 17 Harvard: 17 de Gaulle: 17 Bundy: 17 the Great Society: 17 Asians: 17 the Berlin Wall: 17 Helsinki: 17 MS: 17 the West Bank: 17 the European Union: 17 n’t: 17 Al Qaida: 17 Abbott: 17 Mint: 16 the Independence of the United States of America: 16 the Department of War: 16 Christianity: 16 Administrations: 16 Belgian: 16 Siam: 16 Stevenson: 16 Europeans: 16 Tehuantepec: 16 North and South America: 16 Marine Corps: 16 the Northern States: 16 Lecompton: 16 New England: 16 Nevada: 16 Sherman: 16 the War of the Rebellion: 16 Argentine: 16 North Dakota: 16 Queen: 16 George: 16 Pittsburgh: 16 the Federal Reserve System: 16 the Border Patrol: 16 the Reconstruction Finance Corporation: 16 Tonight: 16 Sicily: 16 the National Security Council: 16 Red China: 16 the cold war: 16 Romney: 16 Kevin: 16 Kabul: 16 U.S.A.: 16 INF: 16 UK: 16 Second Amendment: 16 Matt: 16 Soleimani: 16 BRYAN: 16 Catholic Majesty: 15 Nashville: 15 Portuguese: 15 Lee: 15 the State of Maine: 15 the west coast: 15 the State of South Carolina: 15 Hamilton: 15 the Executive Departments: 15 the Pension Office: 15 the Government of Mexico: 15 New Granada: 15 Mormons: 15 the United States Navy: 15 the Indian Territory: 15 the Civil Service Commission: 15 Ross: 15 National Guard: 15 Argentina: 15 the Red Cross: 15 Orient: 15 the Gulf Coast: 15 Tribunal: 15 the United States Supreme Court: 15 Balkans: 15 cooperatives: 15 the Federal Reserve: 15 Chief Justice: 15 the Republic of Korea: 15 U.S.S.R.: 15 the Arab States: 15 ABC News: 15 Mars: 15 Peace Corps: 15 Soviet-American: 15 the Voting Rights Act: 15 Head Start: 15 Notre Dame: 15 Amy: 15 African-American: 15 African-Americans: 15 Native American: 15 Hamas: 15 Jesse: 15 Apple: 15 CNN: 15 Greenwood: 15 fed: 14 the Government of Great Britain: 14 St. Petersburg: 14 Chief Magistrate: 14 the Articles of Confederation: 14 Delegate: 14 Mark: 14 the Southwest Branch: 14 Caracas: 14 El Paso: 14 the State of Tennessee: 14 the Territory of Kansas: 14 Persia: 14 The Postmaster-General: 14 Fort Sumter: 14 the Indian Bureau: 14 Gettysburg: 14 Idaho: 14 Revised Statutes: 14 Civil War: 14 Concord: 14 Egan: 14 the Suez Canal: 14 Guam: 14 Manchuria: 14 Huerta: 14 the League of Nations: 14 Chiang Kai-shek: 14 Poles: 14 Bryan: 14 the Soviet Government: 14 GI: 14 Dwight Eisenhower: 14 South Asia: 14 the Alliance for Progress: 14 Peter: 14 the U.N. Security Council: 14 Mideast: 14 PLO: 14 Wieghart: 14 Cheney: 14 Sonia: 14 al Qaeda’s: 14 Natoma: 14 ISIL: 14 the World Trade Organization: 14 MS-13: 14 Donald Trump: 14 ICE: 14 Hitherto: 13 Creeks: 13 James: 13 Sovereign: 13 Lawrence: 13 the Government of France: 13 Buenos Aires: 13 Naples: 13 Buffalo: 13 the Lake of the Woods: 13 Caroline: 13 Pinckney: 13 ANDREW JOHNSON: 13 Central: 13 the Caribbean Sea: 13 Uruguay: 13 the Colombian Government: 13 Kongo: 13 the Hawaiian Islands: 13 Malmros: 13 Hemisphere: 13 Dwight D. Eisenhower: 13 Committee: 13 the League of Women Voters: 13 Pat: 13 the Truman Administration: 13 Fulbright: 13 East Germany: 13 the Common Market: 13 Wallace: 13 Hollywood: 13 Air Force One: 13 START: 13 Main Street: 13 Sawyer: 13 Qadhafi: 13 the Joint Chiefs: 13 Saddam Hussein's: 13 John Boehner: 13 the Post-Office: 12 UnitedStates: 12 Powers: 12 Franklin: 12 senate: 12 Norfolk: 12 the Military Academy: 12 the Treaty of Ghent: 12 Pensacola: 12 Canton: 12 Columbus: 12 Austrian: 12 Canadians: 12 the Republic of Texas: 12 Islands: 12 Christendom: 12 the American Republics: 12 Lima: 12 Regular Army: 12 the Pacific coast: 12 Lexington: 12 the Wilmot Proviso: 12 Webster: 12 A Proclamation Whereas: 12 the Netherlands: 12 Venezuelan: 12 section 3: 12 Yorktown: 12 LOOMIS: 12 the Bureau of Corporations: 12 Finland: 12 the Canal Zone: 12 Payne: 12 Rumania: 12 the Coast Guard: 12 the American Nation: 12 the Farm Board: 12 the Democratic House of Representatives: 12 the Export-Import Bank: 12 General de Gaulle: 12 NATO Allies: 12 SEATO: 12 the Atlantic Alliance: 12 A-11: 12 Kosygin: 12 the Korean Peninsula: 12 Sinai: 12 SS-20: 12 Kraft: 12 Moon: 12 Tucson: 12 Washington State: 12 Noriega: 12 Audience: 12 David: 12 Gabby: 12 Alan: 12 Laughs: 12 DR: 12 Alexandria: 11 Fellow-Citizens: 11 Cherokee: 11 the United States: 11 Algiers: 11 Tunis: 11 republics: 11 Barbary: 11 Spaniards: 11 the Secretaries of War: 11 Sultan: 11 Porte: 11 Territorial: 11 Granada: 11 the British Provinces: 11 the American Union: 11 Halls Stream: 11 Collins: 11 the Oregon Territory: 11 the Del Norte: 11 Dutch: 11 the Naval Academy: 11 Amazon: 11 Ann: 11 Ulysses S. Grant: 11 the Italian Government: 11 NAVY DEPARTMENT: 11 Egyptian: 11 Jamestown: 11 Seattle: 11 Bering Sea: 11 Latin-American: 11 Minneapolis: 11 un-American: 11 the Philippine Islands: 11 Loomis: 11 The Republic of Panama: 11 the Great War: 11 the Holy Land: 11 the Reconstruction Corporation: 11 Tehran: 11 Franklin D. Roosevelt: 11 the International Monetary Fund: 11 Mitchell: 11 Joshua: 11 the United Nations General Assembly: 11 Polaris: 11 Marxist: 11 the Voice of America: 11 Klan: 11 University: 11 Goldberg: 11 Miami: 11 Robert Frost: 11 Thomas: 11 the Civil Rights Act: 11 North Korean: 11 Trewhitt: 11 KRAFT: 11 Libyan: 11 Boeing: 11 Barnes: 11 Olympics: 11 Armageddon: 11 Washington, DC: 11 Dukakis: 11 Latino: 11 McCain: 11 African Americans: 11 Tom: 11 HHS: 11 It's: 11 Obamacare: 11 Maduro: 11 Doug: 11 Roche: 11 Wuhan: 11 Jon: 11 Universe: 10 Niagara: 10 Law: 10 the Atlantic States: 10 The House of Representatives: 10 The British Government: 10 Church: 10 concord: 10 Imperial Majesty: 10 the French Revolution: 10 Portland: 10 The Constitution of the United States: 10 Newfoundland: 10 Southwest: 10 the Western States: 10 Santa Fe: 10 Georgetown: 10 Smith: 10 Cincinnati: 10 Liverpool: 10 the Federal Union: 10 the States and Territories: 10 Vermont: 10 the supreme court: 10 Graham: 10 the Northwest Branch: 10 Richmond: 10 the Atlantic Ocean: 10 the Hudsons Bay Company: 10 Californias: 10 Amistad: 10 Polk: 10 Valley: 10 Key West: 10 the Northwestern Territory: 10 Hungarian: 10 Missouri Compromise: 10 the moon: 10 Jamaica: 10 Powell: 10 Stevens: 10 the Judiciary Committee: 10 Confederate: 10 WAR DEPARTMENT: 10 Adjutant: 10 the General of the Army: 10 San Antonio: 10 the South Pacific: 10 St. Paul: 10 the Bureau of Education: 10 the Court of Claims: 10 the Smithsonian Institution: 10 Wyoming: 10 the Indian Ocean: 10 section 6: 10 Apia: 10 the Governments of the United States: 10 British Columbia: 10 Oklahoma City: 10 the Democratic party: 10 the Agricultural Department: 10 Armenia: 10 Guantanamo Bay: 10 the Department of Labor: 10 the Secret Service: 10 the Emancipation Proclamation: 10 Tampico: 10 Herbert Hoover: 10 the Tennessee Valley: 10 the Democratic National Committee: 10 Hoover: 10 Party: 10 Malaya: 10 Mussolini: 10 the Rules Committee: 10 the Korean War: 10 International Communism: 10 Free World: 10 Humphrey: 10 Vanocur: 10 Dean: 10 non-Communist: 10 the World Health Organization: 10 John Fitzgerald Kennedy: 10 the Republican Convention: 10 Eugene Black: 10 Bunker: 10 U.S.-Soviet: 10 the Vietnam war: 10 World War I: 10 Sam Rayburn: 10 Security Council: 10 John Dean: 10 Shultz: 10 Haldeman: 10 Independents: 10 DREW: 10 Solidarity: 10 Sandinistas: 10 Beijing: 10
Soviet Union: 10 Desert Storm: 10 Somalis: 10 Milosevic: 10 Scout: 10 Sunni: 10 Boy Scouts: 10 Mike Pence: 10 Navigation: 9 Tribes: 9 St. Croix: 9 THE UNITED STATES: 9 Savannah: 9 Java: 9 Lewis: 9 Britannic Majesty's: 9 Province: 9 Lisbon: 9 the Black Sea: 9 Greeks: 9 the Columbia River: 9 the Ohio River: 9 Houses of the Congress: 9 Bank of the United States: 9 the Eastern States: 9 Department of the Government: 9 the Ottoman Empire: 9 John Quincy Adams: 9 the War of the Revolution: 9 the Detroit River: 9 European Governments: 9 the Hawaiian Government: 9 the Union of the States: 9 Prussian: 9 the War and Navy Departments: 9 Napoleon: 9 American Republics: 9 the Republic of Nicaragua: 9 Independence: 9 the Panama Railroad Company: 9 the Pacific Railroad: 9 Johnston: 9 Peck: 9 Anderson: 9 the United States of Colombia: 9 Engineers: 9 the Pacific States: 9 the Freedmen's Bureau: 9 Brownsville: 9 HAMILTON: 9 section 4: 9 Berne: 9 Hebrew: 9 the Government of Italy: 9 Acapulco: 9 a United States: 9 Itata: 9 Schley: 9 Jr.: 9 Guantanamo: 9 the Dominion Government: 9 JOHN HAY: 9 the Customs Tariff of the Republic of Cuba: 9 Cartagena: 9 Clark: 9 The Nation: 9 Reynolds: 9 Bishop: 9 Ottawa: 9 The Red Cross: 9 Albania: 9 the Federal Reserve Act: 9 the Shipping Board: 9 the Bill of Rights: 9 Serbs: 9 Veterans: 9 the Senate Foreign Relations Committee: 9 Des Moines: 9 the Recovery Act: 9 Nazism: 9 General Eisenhower: 9 Harriman: 9 Crimea: 9 Iwo Jima: 9 the Veterans Administration: 9 Mayor: 9 West Germany: 9 the Defense Production Act: 9 the Boy Scouts: 9 Dulles: 9 Health, Education: 9 Lyndon Johnson: 9 Scranton: 9 Macmillan: 9 Nigeria: 9 Buy American: 9 the Commerce Department: 9 Paul: 9 the Rose Garden: 9 Johns Hopkins: 9 Midwest: 9 Education: 9 Romania: 9 the Democratic Republic of Vietnam: 9 Abrams: 9 Jimmy Carter: 9 Middle Eastern: 9 the Burger Court: 9 Hispanics: 9 ABM: 9 the Congressional Budget Office: 9 Grace: 9 KONDRACKE: 9 Health and Human Services: 9 Bahrain: 9 Colin Powell: 9 GATT: 9 Pakistani: 9 Serbian: 9 Sasha: 9 Ben: 9 Sotomayor: 9 Kim: 9 Asian American: 9 Mnuchin: 9 World War Two: 9 Sonny: 9 Deborah: 9 BIRX: 9 N95: 9 Tulsa: 9 Zelenskyy: 9 the Post Office: 8 andWhereas: 8 premises: 8 the Supreme Ruler: 8 Montgomery: 8 Floridas: 8 Warren: 8 Alexander: 8 the States of Georgia: 8 the American Navy: 8 the Territories of the United States: 8 Louisville: 8 the Ordnance Department: 8 Bermuda: 8 the Sublime Porte: 8 the American Revolution: 8 Supreme: 8 the United States Bank: 8 the Mexican Congress: 8 Farewell Address: 8 Bolivar: 8 Anthony: 8 the organic law: 8 St. John: 8 the Republic of New Granada: 8 Adams: 8 Santa Anna: 8 Catholics: 8 the State of Nicaragua: 8 the Mexican War: 8 the Turkish Government: 8 West Indian: 8 Pacific States: 8 Independence Hall: 8 Springfield: 8 the Empire of Japan: 8 Baltic: 8 the organic act: 8 the State of Panama: 8 Clayton: 8 Walker: 8 the Territory of Utah: 8 Washington Territory: 8 San Jose: 8 Brooklyn: 8 The District of Columbia: 8 Va.: 8 Article II: 8 the Revolutionary War: 8 Australian: 8 Indian Territory: 8 the Japanese Government: 8 Mount Vernon: 8 the Railway Mail Service: 8 The United States Government: 8 Sec: 8 Sydney: 8 the Geological Survey: 8 The War Department: 8 Chinaman: 8 Newark: 8 Trenton: 8 South Dakota: 8 the Independent State: 8 Vancouver: 8 Czar: 8 Riggin: 8 Blaine: 8 Diaz: 8 Negros: 8 the National Capital: 8 Buenaventura: 8 the Reclamation Service: 8 Benson: 8 the Forest Service: 8 the Nation: 8 the Middle West: 8 Muscle Shoals: 8 Coolidge: 8 Bank: 8 Social Security Act: 8 the Executive Branch: 8 Hitlerism: 8 North Atlantic: 8 Tom Paine: 8 Fascist: 8 Coast Guard: 8 the General Assembly of the United Nations: 8 the United Nations Relief and Rehabilitation Administration: 8 Julie: 8 communist: 8 The Middle East: 8 Hubert Humphrey: 8 Richard: 8 NBC News: 8 Rockefeller: 8 U-2: 8 SHADEL: 8 Pescadores: 8 the Marshall Plan: 8 UN: 8 Olympic: 8 East-West: 8 Malaysia: 8 Appalachia: 8 Hilsman: 8 Panamanian: 8 the Panama Canal Zone: 8 Lop Nor: 8 the East Room: 8 Vance: 8 the West Coast: 8 the Republic of Vietnam: 8 NLF: 8 Gerald Ford: 8 the Special Prosecutor: 8 Frankel: 8 FRANKEL: 8 Rabin: 8 Rhodesia: 8 Maynard: 8 B-1: 8 Warnke: 8 Persian Gulf: 8 Teddy Roosevelt: 8 MX: 8 Omaha Beach: 8 General Motors: 8 EPA: 8 KALB: 8 Holocaust: 8 Jim Baker: 8 Reykjavik: 8 John Mashek: 8 Saudi: 8 Kuwaiti: 8 Byelorussia: 8 the Iraq war: 8 Somali: 8 Belfast: 8 Kassebaum: 8 George W. Bush: 8 U-S-A: 8 Boehner: 8 Shi'a: 8 the Department of Homeland Security: 8 Homeland Security: 8 Malia: 8 Chuck: 8 the Affordable Care Act: 8 Asian Americans: 8 Walmart: 8 Pence: 8 Kevin McCarthy: 8 Tim: 8 Hispanic American: 8 al-Baghdadi: 8 Jim Jordan: 8 Chris: 8 Josh: 8 USMCA: 8 Cuomo: 8 LabCorp: 8 Vladimir Putin: 8 Society: 7 Liberty: 7 Fellow: 7 Resolution: 7 Passamaquoddy Bay: 7 St. Domingo: 7 SENATE: 7 Wabash: 7 HOUSE: 7 Mobile: 7 JAMES MADISON: 7 CONGRESS: 7 Rodgers: 7 Upper: 7 Porter: 7 new States: 7 Seminoles: 7 Apollo: 7 Hamburg: 7 the Corps of Engineers: 7 the State of Illinois: 7 the British Parliament: 7 Southern States: 7 New Brunswick: 7 Federal Union: 7 the French Chambers: 7 Belgians: 7 the Constitution: 7 Ottoman: 7 Sioux: 7 District: 7 Thompson: 7 Harrison: 7 Suffice: 7 the States of Maine: 7 Rouses Point: 7 St. Regis: 7 San Jacinto: 7 Balize: 7 The Mexican Government: 7 Slidell: 7 Van Buren: 7 Galveston: 7 the South American States: 7 the Republic of Venezuela: 7 the State of California: 7 the States of Central America: 7 Sioux Indians: 7 the Government of Nicaragua: 7 St. Lawrence: 7 Greytown: 7 Departments of the Government: 7 Bulwer: 7 the Republic of Honduras: 7 Nelson: 7 The Post-Office Department: 7 Victoria: 7 Miramon: 7 Abraham Baldwin: 7 the South American Republics: 7 Vanderbilt: 7 the Potomac River: 7 Dakota: 7 the City of Washington: 7 Federal and State: 7 Hong Kong: 7 North Germany: 7 the Department: 7 the Commissioners of the District of Columbia: 7 the Government of China: 7 Roumania: 7 Pacific Railway: 7 Austria-Hungary: 7 Miles: 7 Puritan: 7 section 1754: 7 Rule III: 7 section 2: 7 Barrundia: 7 Montt: 7 Morgan: 7 the Choctaws and Chickasaws: 7 Evans: 7 Mora: 7 Englishmen: 7 Red Cross: 7 Reyes: 7 Brig: 7 Jolo: 7 Arlington: 7 Exposition: 7 isthmian: 7 Acknowledge: 7 Beaupre': 7 the New Panama Canal Company: 7 Porto Rican: 7 Root: 7 the South Atlantic: 7 Balkan: 7 the Lincoln Memorial: 7 Tariff Commission: 7 the Continental Congress: 7 Latvia: 7 Estonia: 7 the Federal Farm Board: 7 Central Europe: 7 The Democratic Party: 7 Benjamin Franklin: 7 Munich: 7 Greenland: 7 Wassell: 7 Marshal Stalin: 7 the United Nations Organization: 7 REA: 7 the North Atlantic Treaty: 7 Rayburn: 7 the Korean war: 7 the Soviet Union's: 7 the Interstate Highway System: 7 Western European: 7 FHA: 7 the International Atomic Energy Agency: 7 Atlas: 7 the National Science Foundation: 7 the Atomic Energy Commission: 7 Dirksen: 7 Adenauer: 7 the Eisenhower Administration: 7 McGee: 7 Minuteman: 7 Drummond: 7 Al Smith: 7 Eastern European: 7 the Atlantic Community: 7 Webb: 7 Birmingham: 7 Panamanians: 7 Barry Goldwater: 7 McCormack: 7 Lenin: 7 President: 7 the South Vietnamese Government: 7 the Government of South Vietnam: 7 Ho Chi Minh: 7 Bob: 7 the New York Times: 7 Glassboro: 7 United Nations Security Council: 7 Ehrlichman: 7 the Warsaw Pact: 7 Vladivostok: 7 the European Community: 7 Mayaguez: 7 Barbara Walters: 7 Namibia: 7 John Paul II: 7 STONE: 7 O'Neill: 7 Jesus: 7 Sandinista: 7 Gramm-Rudman-Hollings: 7 marijuana: 7 NSC: 7 mosques: 7 Dan Quayle: 7 NIH: 7 Pell: 7 Kurds: 7 ADA: 7 Bill Clinton: 7 the United States Armed Forces: 7 the World Trade Center: 7 the National Institutes of Health: 7 Pell Grants: 7 Laura: 7 Chuck Grassley: 7 the Second Amendment: 7 the American Dream: 7 McConnell: 7 Connie: 7 G20: 7 Kelly: 7 Hillary Clinton: 7 Intel: 7 Trayvon Martin: 7 John Lewis: 7 Paris Climate Accord: 7 Steve Scalise: 7 VFW: 7 Schumer: 7 Louie: 7 SBA: 7 Abbott Laboratories: 7 Johnson &: 7 Jim Inhofe: 7 NASDAQ: 7 Ghani: 7 United: 6 Measures: 6 Sardinia: 6 Creek: 6 Natchez: 6 Institution: 6 New Haven: 6 Austin: 6 Gibraltar: 6 Wilkinson: 6 James Madison: 6 the Danish Government: 6 the British Isles: 6 Hull: 6 the La Plata: 6 the Chesapeake Bay: 6 National Government: 6 Bremen: 6 Plymouth: 6 the Virgin Islands: 6 the State of Alabama: 6 the Congress of the Confederation: 6 Portsmouth: 6 supreme: 6 Maysville: 6 Northeast: 6 The Treasury Department: 6 Treasurer: 6 Federal Governments: 6 the European Governments: 6 Address: 6 the Argentine Confederation: 6 the Federal Executive: 6 Fox: 6 Her Majesty's Government: 6 Roman: 6 Athens: 6 Crown: 6 Buchanan: 6 Lake Pohenagamook: 6 Valentine: 6 Nueces: 6 The Spanish Government: 6 Capital: 6 Corpus Christi: 6 Monterey: 6 the Isthmus of Tehuantepec: 6 German Empire: 6 the Panama Railroad: 6 Peruvian: 6 the Colorado River: 6 Henry: 6 Daniel Webster: 6 the Captain-General: 6 Smyrna: 6 democrats: 6 Adam: 6 Sound: 6 Cyane: 6 the Bay Islands: 6 Fort Leavenworth: 6 Dred Scott: 6 the Nicaraguan Government: 6 John Brown: 6 the National Constitution: 6 Oriental: 6 Andrew Johnson: 6 WILLIAM H. SEWARD: 6 the State of North Carolina: 6 IV: 6 Baird: 6 POST-OFFICE DEPARTMENT: 6 Tex.: 6 The Department of State: 6 the Census Bureau: 6 the Land Office: 6 the Advisory Board: 6 the Government of Venezuela: 6 Melbourne: 6 the Hudson River: 6 the District Commissioners: 6 Swiss: 6 the Comptroller of the Currency: 6 Apaches: 6 the Weather Bureau: 6 Jeannette: 6 Siberia: 6 The Chinese Government: 6 the Government of Colombia: 6 the Ecuadorian Government: 6 the Caroline Islands: 6 Rock Springs: 6 Barbados: 6 Guthrie: 6 the International American Conference: 6 the State of Nebraska: 6 Guiana: 6 the Associated Press: 6 The Department of Justice: 6 Yankee: 6 the Eleventh Census: 6 British Guiana: 6 the North Pacific: 6 Mataafa: 6 Armenians: 6 the committee of safety: 6 Indianapolis: 6 Woodford: 6 Marietta: 6 Marblehead: 6 Boxers: 6 Pan-American: 6 Yellowstone: 6 the Isthmian Canal: 6 the General Board: 6 the University of Virginia: 6 The White House: 6 NASHVILLE: 6 HAY: 6 TELEGRAM - TRANSLATION: 6 BOGOTA: 6 Latin: 6 Henry Cabot Lodge: 6 La Boca: 6 Delta: 6 Panama Canal: 6 NEGRO: 6 THE WHITE HOUSE: 6 U.S.S.: 6 the Federal Trade Commission: 6 the Democratic Congress: 6 Insofar: 6 Abraham: 6 the Public Treasury: 6 Federal, State: 6 Princeton: 6 the Ways and Means Committee: 6 Mary: 6 Comfort: 6 Nazi Germany: 6 West Africa: 6 the Southwest Pacific: 6 the United Nations': 6 Fascists: 6 the United Mine Workers: 6 the War Labor Board: 6 Nimitz: 6 Dumbarton Oaks: 6 Potsdam: 6 World War I.: 6 the Greek Government: 6 the Charter of the United Nations: 6 the First World War: 6 Isaiah: 6 State Department: 6 Pennsylvania Avenue: 6 Thou: 6 the United States Information Agency: 6 Reserve: 6 the Party of the Future: 6 the Free World's: 6 the International Court of Justice: 6 ABC: 6 Herter: 6 Gardner: 6 OECD: 6 Marx: 6 Harvard University: 6 the Central Intelligence Agency: 6 Rice: 6 Gromyko: 6 the Army Corps of Engineers: 6 Salinger: 6 these United Nations: 6 the House Judiciary Committee: 6 the Kennedy Round: 6 Gallery: 6 Bennett: 6 the Arab League: 6 Quayle: 6 Success: 6 the Geneva accords: 6 Tulane: 6 Gannon: 6 VALERIANI: 6 George Marshall: 6 the People's Republic: 6 Backfire: 6 MIA: 6 MAYNARD: 6 Walter Mondale: 6 Trident: 6 Sun: 6 Southwest Asia: 6 Fed: 6 Ballyporeen: 6 the U.N. Charter: 6 the Camp David accords: 6 Mall: 6 Kelley: 6 DEA: 6 Jack Kemp: 6 Berliner: 6 Little League: 6 Jim Lehrer: 6 the Gulf war: 6 the Arabian Peninsula: 6 the Baltic States: 6 Viva Bush: 6 Compton: 6 Sarajevo: 6 Bosnian: 6 Matthew: 6 Kosovar: 6 the Violence Against Women Act: 6 Patton: 6 Shia: 6 Keeping America: 6 Phoenix: 6 Jill Biden: 6 Christina: 6 Jessica: 6 Nicole: 6 Ferguson: 6 Megan: 6 Scouts: 6 Klaus: 6 Melania: 6 NBC: 6 Kim Jong Un: 6 Alice: 6 Qasem Soleimani: 6 Gavin Newsom: 6 Scalia: 6 Mother Fletcher: 6 G7: 6 the Government of the Union: 5 Gentlemen: 5 the proper Department: 5 the East Indies: 5 the Bay of Fundy: 5 Hudson: 5 Rio Bravo: 5 Northern: 5 Kaskaskia: 5 the States or Territories: 5 Shall: 5 Aaron Burr: 5 the Union of these States: 5 Choctaws: 5 WRT: 5 Milan: 5 the United Kingdom of Great Britain: 5 Halifax: 5 York: 5 Lake Ontario: 5 the St. Lawrence: 5 the American Army: 5 Major-General: 5 Amelia Island: 5 St. Augustine: 5 Chili: 5 Cortes: 5 The Military Academy: 5 Federal Constitution: 5 Congresses: 5 the European States: 5 the States of this Union: 5 Portland Canal: 5 the Empire of Brazil: 5 the American Republic: 5 Western States: 5 Bahamas: 5 the Portuguese Government: 5 Journal: 5 The Congress of the United States: 5 Muscat: 5 Jesup: 5 Spanish America: 5 The Federal Constitution: 5 Dallas: 5 A Proclamation : 5 Lake Superior: 5 the Bank of England: 5 Forsyth: 5 WASHINGTON: 5 Alexander McLeod: 5 our Declaration of Independence: 5 Her Britannic Majesty: 5 Woods: 5 state: 5 William: 5 Jones: 5 Rio: 5 the South Pass: 5 the States of Mississippi: 5 moment's: 5 Gila: 5 theGovernment: 5 Mosquito Indians: 5 the Mississippi Valley: 5 North American Provinces: 5 the Central American States: 5 San Juan del Norte: 5 the Spanish American Republics: 5 Frenchmen: 5 the Ordinance of ': 5 Punta Arenas: 5 Milwaukee: 5 the Department for: 5 eleven States: 5 the Mosquito Coast: 5 the Territories of New Mexico: 5 Brigham Young: 5 Sonora: 5 the Colonization Society: 5 Howard: 5 Marcy: 5 the Spanish American States: 5 Roger Sherman: 5 William Few: 5 John Langdon: 5 George Read: 5 Alexander Hamilton: 5 Harper's Ferry: 5 National Constitution: 5 the Illinois River: 5 God's: 5 EDWIN M. STANTON: 5 the Executive Mansion: 5 Sheridan: 5 the Isthmus of Darien: 5 Copenhagen: 5 West India Islands: 5 Miranda: 5 the Gulf States: 5 Antilles: 5 the Missouri River: 5 Quincy: 5 the Washington Monument: 5 The Japanese Government: 5 The Department of the Interior: 5 Servia: 5 Moslem: 5 Khedive: 5 Garfield: 5 The State Department: 5 the Republic of Liberia: 5 Iglesias: 5 Seoul: 5 Miantonomoh: 5 the Nicaragua Canal: 5 Wyoming Territory: 5 Chinamen: 5 Van Bokkelen: 5 the Marine-Hospital Service: 5 Navy Department: 5 Bennington: 5 Johnstown: 5 Guatemalan: 5 the Republic of Brazil: 5 a Provisional Government: 5 the Medical Corps: 5 Sandy Hook: 5 Dominican Republic: 5 The Civil Service Commission: 5 Armenian: 5 the Bureau of Animal Industry: 5 Grover Cleveland: 5 Managua: 5 The Interstate Commerce Commission: 5 the Republic of Hawaii: 5 the Pribilof Islands: 5 Wheeler: 5 the Canal Commission: 5 Chamizal: 5 Azores: 5 the Capital of the Nation: 5 the Notification Committee: 5 the Tsung-li Yamen: 5 Empress: 5 Conger: 5 Old World: 5 District of Columbia: 5 Tower: 5 the General Staff: 5 Jew: 5 the Golden Rule: 5 the Spanish war: 5 Gorgas: 5 ARMY: 5 Tawney: 5 the Zelaya Government: 5 Far Eastern: 5 Arctic: 5 Scotch: 5 Colorado River: 5 Mexico City: 5 Versailles: 5 Harding: 5 the Continental Army: 5 the British Empire: 5 Cornwallis: 5 John Wise: 5 Article 5: 5 the House Ways and Means Committee: 5 the Public Health Service: 5 the Federal Power Commission: 5 Valley Forge: 5 Corporation: 5 Happiness: 5 N.R.A.: 5 the Democratic Convention: 5 the Rural Electrification Administration: 5 Denver: 5 Western Hemisphere: 5 New Guinea: 5 Casablanca: 5 Bethlehem: 5 the Atlantic Charter: 5 the Crimea Conference: 5 Malta: 5 Budget Message: 5 non-Federal: 5 the Commodity Credit Corporation: 5 the Housing Act: 5 the Congressional Record: 5 the Blair House: 5 the Cabinet Room: 5 Western Germany: 5 Legislative Branches: 5 The State of the Union: 5 Organization: 5 Jupiter: 5 the New Frontier: 5 Baptist: 5 Alamo: 5 the Foreign Relations Committee: 5 LEVY: 5 Von Fremd: 5 Morton: 5 Guinea: 5 Sputnik: 5 Thomas Paine: 5 Karl Marx: 5 Press: 5 the Internal Revenue Service: 5 Central and Eastern Europe: 5 Dag Hammarskjold: 5 Laotian: 5 Gleason: 5 The Alliance for Progress: 5 Alliance for Progress: 5 the Office of the Presidency: 5 Cape Canaveral: 5 --And: 5 Vanderbilt University: 5 Moss: 5 Viet Cong: 5 Bobby Baker: 5 Keating: 5 Dean Rusk: 5 Erhard: 5 the Gulf of Tonkin: 5 Morrison: 5 Russell: 5 Allen: 5 the Tonkin Gulf: 5 the American Negro: 5 USIA: 5 the SEATO Treaty: 5 Nation's Capital: 5 Turner: 5 Bosch: 5 Schlesinger: 5 World Wars: 5 the U.S. Court of Appeals: 5 Cambodian: 5 the Statue of Liberty: 5 St. Clair: 5 Petersen: 5 Gerald R. Ford: 5 FDR: 5 Tulane University: 5 Bicentennial: 5 the Pacific Basin: 5 East Asia: 5 Jack: 5 the Wall Street Journal: 5 a Cold War: 5 Holiness: 5 the Washington Post: 5 the Industrial Revolution: 5 Embassy: 5 Stone: 5 Phil Gramm: 5 County Tipperary: 5 the Caribbean Basin: 5 the Lebanese Government: 5 Somoza: 5 Fidel Castro: 5 the Organization of Eastern Caribbean States: 5 the Congressional Medal of Honor: 5 Rangers: 5 East Berlin: 5 Raise: 5 contras: 5 Kalb: 5 Manhattan: 5 Midgetman: 5 Kohl: 5 Nunn: 5 Shevardnadze: 5 Ukrainians: 5 Sundance: 5 Michael: 5 JENNINGS: 5 ACLU: 5 Lloyd Bentsen: 5 the Universal Declaration of Human Rights: 5 Lisa: 5 Baltics: 5 Ann Compton: 5 Sandy: 5 Silicon Valley: 5 the Department of Education: 5 Abbas: 5 Belarus: 5 the Federal Emergency Management Agency: 5 Native Americans: 5 the Chemical Weapons Convention: 5 NRA: 5 IRS: 5 Tutsi: 5 Kenya: 5 the Global Fund: 5 al Qaeda's: 5 Hezbollah: 5 Ted Kennedy: 5 Damascus: 5 ROTC: 5 Iraqi Army: 5 Operation Iraqi Freedom: 5 Michelle Obama: 5 he's: 5 God Bless: 5 Barack Obama: 5 Gitmo: 5 Lilly: 5 the Islamic Republic of Iran: 5 BP: 5 Brandon: 5 Bin Laden: 5 Aurora: 5 Trans-Pacific Partnership: 5 the Civil Rights Movement: 5 Cory: 5 CVS: 5 Jim Crow: 5 Clem: 5 Antonin Scalia: 5 Justin: 5 Tim Scott: 5 DACA: 5 Parkland: 5 Mark Meadows: 5 the Space Force: 5 JONES: 5 Abe: 5 the United States Military: 5 Chuck Schumer: 5 Mueller: 5 James Lankford: 5 Quest: 5 Brian Kemp: 5 the Paycheck Protection Program: 5 Lindy: 5 Blinken: 5 The American Jobs Plan: 5 Van Ellis: 5 MAGA: 5 the Executive Department: 4 Officers: 4 Consuls: 4 Spirit: 4 Senekas: 4 Antwerp: 4 Appropriations: 4 Dinsmoor: 4 Articles: 4 the Executive Directory: 4 the Supreme Being: 4 Dick: 4 Chickasaws: 4 Louisana: 4 Orleans: 4 Freeman: 4 Frankfort: 4 Barron: 4 Cape Cod: 4 un: 4 the Michigan Territory: 4 the Barbary Powers: 4 awaken: 4 Upper Canada: 4 Gaines: 4 The Bank of the United States: 4 the State Governments: 4 Perry: 4 St. Marys: 4 the King of Sweden: 4 Norwegian: 4 the Republics of Colombia: 4 Ashley: 4 Leavenworth: 4 Hanseatic: 4 the War of Independence: 4 Turks: 4 Cook: 4 the Delaware River: 4 Choctaw: 4 the Navy Board: 4 Territories of: 4 McCulloch: 4 the Turkish Empire: 4 freeman: 4 Crawford: 4 Secretaries: 4 Great Lakes: 4 Call: 4 British Provinces: 4 the Russian Government: 4 the Cherokee Nation: 4 esq.: 4 Ellis: 4 Sandwich: 4 Long Island: 4 democrat: 4 the Indian Department: 4 the States of Louisiana: 4 Quito: 4 the Brazilian Government: 4 The Navy Department: 4 St. Johns: 4 Tyler: 4 JAMES BUCHANAN: 4 Bouchette: 4 the Governments of Europe: 4 the Sandwich Islands: 4 Congress the Executive: 4 Dorr: 4 Vancouvers Island: 4 Californian: 4 the Western States and Territories: 4 Trist: 4 the Superintendent of the Coast Survey: 4 the German States: 4 the Government of New Granada: 4 Salt Lake: 4 Sacramento: 4 the Central American Republics: 4 Territory of the United States: 4 The Government of the United States: 4 Senatorial: 4 the Red Sea: 4 the Governments of Great Britain: 4 La Plata: 4 the Coast Survey: 4 the Republic: 4 Rivas: 4 KEEP: 4 Proviso: 4 the States of South Carolina: 4 PRINCIPLE: 4 Marion: 4 Andre: 4 Toledo: 4 the Territories of Nebraska: 4 Pawnee City: 4 the Shawnee Mission: 4 the Departments of State: 4 Aspinwall: 4 theConstitution: 4 the Mormon Church: 4 Calhoun: 4 Harney: 4 Mormon: 4 PRESIDENT: 4 Roger: 4 Echo: 4 Senators and Representatives: 4 the Governor-General: 4 the Ordinance of '87: 4 Ferry: 4 Southampton: 4 John Brown's: 4 Mosquito: 4 Fort Pickens: 4 Tropics: 4 Liberian: 4 Jefferson Davis: 4 the Homestead Act: 4 TREASURY DEPARTMENT: 4 General Grant's: 4 Executive Department: 4 section 1: 4 Madagascar: 4 Washburn: 4 the Swiss Republic: 4 Lake Michigan: 4 the Atlantic Seaboard: 4 War Department: 4 Spartanburg: 4 the Army Medical Museum: 4 the Atlantic Coast: 4 the Southern Claims Commission: 4 the American Congress: 4 Venus: 4 New South Wales: 4 Mississippi River: 4 Strong: 4 Black Hills: 4 the Commissioners of the District: 4 the Ta Tsing Empire: 4 sec: 4 Fortune Bay: 4 Library: 4 the Signal Corps: 4 The German Government: 4 Sierra Leone: 4 Santo Domingo: 4 British North America: 4 Amphitrite: 4 Alert: 4 Hongkong: 4 Haytian: 4 Isthmian: 4 New York Harbor: 4 Cutting: 4 the Union Army: 4 the Government Treasury: 4 Clerk: 4 the Postmaster-General's: 4 Field: 4 John F. Hartranft: 4 Muscogee: 4 Ponca: 4 the Western Continent: 4 the Kingdom of Hawaii: 4 The Provisional Government: 4 the Balmaceda Government: 4 Columbian Exposition: 4 World: 4 Pearl River: 4 Navassa: 4 the Bureau of Indian Affairs: 4 the Cherokee Strip: 4 the States of electors: 4 North Americans: 4 The Democratic party: 4 Bessemer: 4 the Fish Commission: 4 the Tribunal of Arbitration: 4 the City of New York: 4 the Union Pacific Railway: 4 the supreme court of the District: 4 the United States Civil Service Commission: 4 Fair: 4 Willis: 4 GROVER CLEVELAND: 4 Cienfuegos: 4 Atua: 4 Tientsin: 4 the national Government: 4 Merry: 4 the Greater Republic of Central America: 4 Diet: 4 the Revenue Act: 4 Cardenas: 4 Bahia Honda: 4 Manila Bay: 4 Admiral Dewey: 4 Cervera: 4 M. Cambon: 4 the St. Lawrence River: 4 Miller: 4 the Twelfth Census: 4 the Republics of Nicaragua: 4 the Government of Honduras: 4 the Isthmian Canal Commission: 4 Lynn Canal: 4 the Spanish War: 4 Dewey: 4 Taku: 4 the Court of Arbitration: 4 the Western Pacific: 4 the American Bar Association: 4 Plenipotentiaries: 4 a. m.: 4 Tobar: 4 STATE: 4 BOYD: 4 ARIAS: 4 Varilla: 4 MOODY: 4 The Department of Commerce and Labor: 4
Mo.: 4 The Government of the Republic of Panama: 4 the Bureau of Labor: 4 Algeria: 4 Interstate Commerce Commission: 4 Burke: 4 the Dominican Government: 4 Island: 4 Porto Ricans: 4 Gatun: 4 the New York: 4 Robert E. Lee: 4 the Central Government: 4 D.: 4 Corps of Engineers: 4 the Culebra Cut: 4 Schedule K: 4 International Prize Court: 4 LATIN AMERICA: 4 Zelaya: 4 Federal court: 4 the Monetary Commission: 4 Montenegro: 4 the Tariff Board: 4 Tariff Board: 4 Utica: 4 The Declaration of Independence: 4 Arabic: 4 the Central Powers: 4 Tenth: 4 the District Court: 4 the Central Empires: 4 Theirs: 4 Napoleonic: 4 boycotts: 4 the War Finance Corporation: 4 Howard University: 4 the American Legion: 4 Chatham: 4 the Constitutional Convention: 4 Hartford: 4 the Washington Conference: 4 bas: 4 the Liberty Bell: 4 The Department of Commerce: 4 the North Atlantic: 4 the Northwest Territory: 4 Stimson: 4 the United States Public Health Service: 4 The Department of Labor: 4 the World Court: 4 the National Credit Association: 4 CCC: 4 the Farm Security Administration: 4 Socialist: 4 Commanders: 4 the Pacific area: 4 Indo-China: 4 Stalingrad: 4 Selective Service: 4 Leahy: 4 Arnold: 4 Guadalcanal: 4 the Catholic Church: 4 Polish Provisional Government of National Unity: 4 Hiroshima: 4 the Fair Labor Standards Act: 4 the Labor-Management Relations Act: 4 the Reciprocal Trade Agreements Act: 4 South Korean: 4 Tibet: 4 Nationalist: 4 Chinese Communists: 4 Matthew Ridgway: 4 Murphy: 4 the Republican National Committee: 4 Adlai Stevenson: 4 Acheson: 4 Bradley: 4 the North Atlantic Treaty Organization: 4 this General Assembly: 4 TNT: 4 the Commission on Intergovernmental Relations: 4 the Small Business Administration: 4 the Department of Health, Education: 4 the Baghdad Pact: 4 the Organization for Trade Cooperation: 4 the Federal Civil Defense Administration: 4 Thor: 4 Mutual Security: 4 Richard M. Nixon: 4 CBS News: 4 Novins: 4 Uncle Sam: 4 United Press International: 4 Levy: 4 Ridgway: 4 Cater: 4 CATER: 4 Tampa: 4 Howe: 4 Godspeed: 4 Martin: 4 the First Amendment: 4 Hemispheric: 4 Victorian: 4 Commandant: 4 Gray: 4 The Marshall Plan: 4 Stanford: 4 World Bank: 4 Bell: 4 Saturn: 4 John Glenn: 4 Guard: 4 the South China Sea: 4 Nassau: 4 the Supreme Court's: 4 the Federal Republic: 4 Frost: 4 John Kennedy's: 4 Khanh: 4 Calif.: 4 J. Edgar Hoover: 4 Mann: 4 Al: 4 Bonn: 4 Mansfield: 4 The Great Society: 4 the Armed Forces of the United States: 4 Aggression: 4 Lady Bird: 4 Vandenberg: 4 Sinkiang: 4 Depression: 4 the United Arab Republic: 4 Driver: 4 Moyers: 4 Gus Grissom: 4 Gemini: 4 George Miller: 4 Garland: 4 Schools: 4 Dominicans: 4 AID: 4 Averell Harriman: 4 Asian Development Bank: 4 a United Nations: 4 Thurgood Marshall: 4 Hatfield: 4 Foley: 4 Department of Transportation: 4 Gallup: 4 Frank: 4 Time Magazine: 4 DMZ: 4 Model Cities: 4 the national committee: 4 Cyrus Vance: 4 Throckmorton: 4 Harlem: 4 Ho Chi Minh's: 4 Williamsburg: 4 McCarthy: 4 the Safe Streets Act: 4 the National Liberation Front: 4 Kappel: 4 the White House Staff: 4 John Ehrlichman: 4 Kleindienst: 4 Simon: 4 the Union Address: 4 T.R.: 4 these United States of America: 4 the League of Women Voters Education Fund: 4 Valeriani: 4 F-14: 4 Playboy: 4 NELSON: 4 HUD: 4 Moore: 4 the Jordan River: 4 Embassies: 4 Garter: 4 the Environmental Protection Agency: 4 Life, Liberty: 4 Knute Rockne: 4 Gipper: 4 Lech Walesa: 4 Salvadoran: 4 747: 4 Aeroflot: 4 The Joint Chiefs: 4 Grenadian: 4 Rosa Parks: 4 Tyre: 4 Don Regan: 4 Superfund: 4 Fudan University: 4 Mitterrand: 4 Tipperary: 4 Prague: 4 Independence Day: 4 God Bless America: 4 Dorothy Ridings: 4 Diane Sawyer: 4 Congressional Budget Office: 4 Jerry Falwell: 4 Simpson-Mazzoli: 4 James Baker: 4 Amtrak: 4 Torah: 4 Bergen-Belsen: 4 Anne: 4 Challenger: 4 Butch: 4 Barbara Bush: 4 Zimbabwe: 4 Peter Jennings: 4 the East Coast: 4 the Pledge of Allegiance: 4 U.N. Security Council: 4 UVA: 4 McKenzie: 4 Joe Barton: 4 Aggie: 4 Texas A&M: 4 Dick Cheney: 4 The Security Council: 4 UAE: 4 Jackie: 4 Qatar: 4 the G - 7: 4 Bessmertnykh: 4 the Paris Charter: 4 COCOM: 4 Boris Yeltsin: 4 Yeltsin: 4 PBS: 4 Mashek: 4 Bosnians: 4 Mogadishu: 4 START II: 4 the Family and Medical Leave Act: 4 the Tax Code: 4 the Democratic Leadership Council: 4 Koran: 4 the Mississippi Delta: 4 Jack Lucas: 4 Croats: 4 the Medicare Trust Fund: 4 McCaffrey: 4 Kyl: 4 Feinstein: 4 Tipper Gore: 4 Gingrich: 4 D’Amato: 4 Jackie Robinson: 4 Moynihan: 4 EITC: 4 401k: 4 WTO: 4 Janet: 4 Max Baucus: 4 Pakistanis: 4 Laura Bush: 4 The Department of Homeland Security: 4 Iranians: 4 Orrin Hatch: 4 the PATRIOT Act: 4 AUDIENCE:: 4 Sharon: 4 Maliki: 4 EU: 4 Yuma: 4 Doha: 4 Sudan: 4 it's: 4 I've: 4 Larry: 4 Fannie: 4 Freddie: 4 Suzanne: 4 PEPFAR: 4 Jenna: 4 Ken Salazar: 4 Thomas Edison: 4 Walter Reed: 4 Maureen: 4 Robert: 4 Benghazi: 4 Qaddafi: 4 Chrysler: 4 the Great Recession: 4 Benjamin: 4 Together: 4 the Paycheck Fairness Act: 4 Bashar al-Assad: 4 TPP: 4 National Intelligence: 4 Neil Gorsuch: 4 Make America Great Again: 4 Rick: 4 North Korea’s: 4 John Cornyn: 4 Kayla: 4 CJ: 4 Otto: 4 Don: 4 CPAC: 4 Death to America: 4 Build: 4 the West Side Highway: 4 Josh Hawley: 4 Mike Pompeo: 4 Judah: 4 Larry Kudlow: 4 Mike Lee: 4 Janiyah: 4 Jody: 4 NCAA: 4 Perry Mason: 4 the Javits Center: 4 Hogan: 4 WHO: 4 Admiral Polowczyk: 4 GM: 4 Sonny Perdue: 4 Bill Bryan: 4 Labor Day: 4 John Kelly: 4 Nord Stream 2: 4 Pfizer: 4 Amy Coney Barrett: 4 Xi Jinping: 4 George Floyd’s: 4 Mother Randle: 4 Act: 3 Community: 3 Cornplanter: 3 Courts: 3 Florins: 3 Amsterdam: 3 theUnited States: 3 Fellow Citizens of the Senate: 3 the General Convention: 3 Vessels: 3 The Treaty: 3 West Florida: 3 Great: 3 Agency: 3 the Revolutionary war: 3 the State of Rhode Island: 3 the Independence of the: 3 pacific: 3 UNITED STATES: 3 Northampton: 3 the State of Pennsylvania: 3 antirepublican: 3 Goodrich: 3 the Barbary States: 3 Pacific Ocean: 3 Vincennes: 3 Clarke: 3 Pittsburg: 3 Burr: 3 Gates: 3 Perdido: 3 Danish: 3 Danish Majesty: 3 JAMES MONROE: 3 Discord: 3 East Florida: 3 Barbary Powers: 3 the Province of Texas: 3 NY: 3 Council Bluffs: 3 the Red River: 3 the Captain-General of: 3 the Netherlands,: 3 the Republic of Chile: 3 the Mediterranean Sea: 3 the Board of Engineers: 3 Lafayette: 3 Labrador: 3 Grecian: 3 Nicholas: 3 Tacubaya: 3 Cass: 3 Ohio Canal: 3 the Ottoman Porte: 3 the Engineer Department: 3 ANDREW JACKSON: 3 the Cape of Good Hope: 3 the Choctaw Nation: 3 State Governments: 3 the Bahama Islands: 3 Chickasaw: 3 the King of the Netherlands: 3 Terceira: 3 Sumatra: 3 Rio Janeiro: 3 the Falkland Islands: 3 American Indians: 3 the Constitution Congress: 3 Beaufort: 3 the Colombian Republic: 3 the American Constitution: 3 the Journal of the Senate: 3 the Chamber of Deputies: 3 the Wabash River: 3 the King of the French: 3 republicans: 3 Nacogdoches: 3 Seminole: 3 the Federal and State Governments: 3 the Peru-Bolivian Confederation: 3 the Alleghany Mountains: 3 Navy Island: 3 Madeira: 3 the Province of New Brunswick: 3 the Government of Her Britannic Majesty: 3 The King of the: 3 The War and Navy Departments: 3 National Treasury: 3 the United States: 3 Red River: 3 Constellation: 3 Dolphin: 3 Lockport: 3 Her Britannic Majesty's Government: 3 The General Government: 3 Constitutions: 3 Scandinavia: 3 the Fiscal Corporation: 3 the English Government: 3 Secretary's: 3 ALBERT SMITH: 3 the St. Francis: 3 Lake Memphremagog: 3 Francis: 3 Nova Scotia: 3 Morris: 3 Macedonian: 3 Suez: 3 Smithsonian: 3 Fort Adams: 3 the electoral college: 3 Memphis: 3 The Department: 3 Shannon: 3 Wise: 3 Alleghanies: 3 the Straits of Fuca: 3 Oregon Territory: 3 the Government of General Herrera: 3 Herrera: 3 Matamoras: 3 the States of our Union: 3 the Provinces of New Mexico: 3 Astoria: 3 Queretaro: 3 the States of Germany: 3 the Government of that Republic: 3 Cape Horn: 3 the State of Wisconsin: 3 Corps: 3 the Union of the United States: 3 States of Central America: 3 Treasury Department: 3 Pacific Coast: 3 The State of Texas: 3 Greenwich: 3 Marsh: 3 Kossuth: 3 John Q. Adams: 3 Nullification: 3 John C. Calhoun: 3 San Juan de Nicaragua: 3 the Peruvian Government: 3 the Amazon River: 3 Constitution and Government: 3 the Constitution of the UnitedStates: 3 GeneralGovernment: 3 Monticello: 3 Koszta: 3 Matanzas: 3 the State of Honduras: 3 the Union as States: 3 NECESSITY: 3 Lake Nicaragua: 3 British Government: 3 the supreme authority: 3 Kickapoo: 3 the Territories of Kansas: 3 harden: 3 Constitutionof: 3 Ruatan: 3 the Bay of Honduras: 3 Shah: 3 Water Witch: 3 the Commanding General: 3 Cal: 3 the United States Constitution: 3 McLean: 3 Chase: 3 the Governments of Nicaragua: 3 Harpers Ferry: 3 Zuloaga: 3 Juarez: 3 Post-Office: 3 Rufus King: 3 Louis Napoleon: 3 Federalist: 3 Frenchman: 3 the National Union: 3 Confederate States: 3 Knoxville: 3 Daniel: 3 Campbell: 3 Mason: 3 Dixon: 3 Port Royal: 3 the Republic of Hayti: 3 the National Executive: 3 San Salvador: 3 the Bureau of Military Justice: 3 Clement C. Clay: 3 North or South: 3 the War Office: 3 Postmaster-General: 3 INTERIOR: 3 Saturday's: 3 Lancaster: 3 Humboldt: 3 section 8: 3 ARTICLE III: 3 Patent Office: 3 the North Pacific Ocean: 3 Hesse: 3 day's: 3 Internal Revenue: 3 the Republic of the United States: 3 Stockholm: 3 the States of South America: 3 The Imperial Government: 3 Danube: 3 Fairfield: 3 Republics of Central: 3 Darien: 3 TREASURY: 3 Quartermaster: 3 Niagara Falls: 3 the General Treasury: 3 the Fine Arts: 3 The Ottoman Government: 3 Virginius: 3 Santiago de Cuba: 3 Patents: 3 Orinoco: 3 White Leagues: 3 South American States: 3 Shreveport: 3 the Black Hills: 3 John Marshall: 3 the Ottoman Government: 3 the Rio Grande River: 3 the Signal Service: 3 Pago-Pago: 3 the Bureau of Statistics: 3 the House of Commons: 3 Hayes: 3 Carlisle: 3 the Pacific Slope: 3 Forest Grove: 3 the Corps of Judge-Advocates: 3 the Record and Pension Division: 3 Sitka: 3 Hampton: 3 Pratt: 3 the Public Lands Commission: 3 Hebrews: 3 the Dutch East Indies: 3 the Argentine Republic and: 3 the Governments of those: 3 the Chief Signal Officer: 3 section 1753: 3 the States of the Western Hemisphere: 3 the Swiss Government: 3 St. Lawrence Bay: 3 North Star: 3 Monadnock: 3 Hampton Roads: 3 Bangkok: 3 the Spanish Antilles: 3 Central and South American: 3 Gulf States: 3 Fellow-Citizens:: 3 the Life Saving Service: 3 Bedloes Island: 3 Australasian: 3 the Department for the last fiscal year: 3 Noncompetitive: 3 Customs: 3 Carlos Butterfield: 3 Benjamin Harrison: 3 BENJ: 3 Persian: 3 Cherokee Outlet: 3 National Congress: 3 Mizner: 3 the Delagoa Bay Railway: 3 the Venezuela Steam Transportation Company: 3 Notice: 3 Balmaceda: 3 Itala: 3 the Congressional party: 3 The Chilean Government: 3 the Maritime Canal Company of Nicaragua: 3 the State of Vermont: 3 Wichita: 3 Keweenaw: 3 Matta: 3 Talbot: 3 Foster: 3 Mole: 3 Irishman: 3 William McKinley: 3 The Republican party: 3 Welsh: 3 Manchester: 3 the Aleutian Islands: 3 John W. Foster: 3 the Welland Canal: 3 Ponape: 3 the Bureau of Military Information: 3 Noble: 3 Raleigh: 3 the Republic of France: 3 the Paris Tribunal of Arbitration: 3 Mosul: 3 Chilean Claims Commission: 3 Waller: 3 the gold reserve: 3 the National Academy of Sciences: 3 Union Pacific: 3 the Kansas Pacific: 3 Pinar del Rio: 3 the American Red Cross: 3 General Shafter's: 3 Cavite: 3 the Cape Verde Islands: 3 Ponce: 3 the Austro-Hungarian Government: 3 the Maritime Canal Company: 3 Cerruti: 3 Admiral: 3 the Government of Japan: 3 the Government of Russia: 3 the Bureau of the American Republics: 3 the Central Pacific: 3 Western Pacific: 3 Hunter: 3 the Bay of Panama: 3 South African: 3 Tallulah: 3 the Tariff Act: 3 Puerto Ricans: 3 Dean C. Worcester: 3 Chairman: 3 Shan-tung: 3 Boxer: 3 Southern Africa: 3 Trinidad: 3 William H. Taft: 3 Stich: 3 Luzon: 3 Witness Whereof: 3 non-American: 3 New World: 3 Naval Militia: 3 Republic of Cuba: 3 Machinery: 3 United States of America: 3 the Cuban Government: 3 the Governments of Colombia: 3 Panama Government: 3 Keep: 3 Herran: 3 DIEHL: 3 DELANO: 3 Elihu Root: 3 the Permanent Court of Arbitration: 3 Subig Bay: 3 the Government of Panama: 3 fig: 3 the Yellowstone Park: 3 the Indian Service: 3 Wright: 3 Governmental: 3 The Hague Convention: 3 the American Continent: 3 the Medical Department: 3 the Roman Catholic Church: 3 Samar: 3 Statehood: 3 the French Canal Company: 3 Great Plains: 3 the White Mountains: 3 Cuban Government: 3 The Hague Conference: 3 Shonts: 3 Jackson Smith: 3 Rogers: 3 Corozal: 3 Hill: 3 Lieut: 3 Blocksom's: 3 Appalachian: 3 Maj: 3 The Biological Survey: 3 the ocean mail act: 3 the world united: 3 socialists: 3 Americanism: 3 Secret Service: 3 POSTAL SAVINGS BANKS: 3 PARCEL POST: 3 PANAMA CANAL: 3 EUROPE: 3 Monrovia: 3 the Washington Conventions: 3 Conventions: 3 Corregidor: 3 Olongapo: 3 DEPARTMENT OF COMMERCE: 3 the International Prize Court: 3 LIBERIA: 3 Civil Service: 3 the Diplomatic Service: 3 a Supreme Court: 3 John Paul Jones: 3 Ellis Island: 3 the Imperial Valley: 3 the Office of Public Buildings and Grounds: 3 Rock Creek Park: 3 Willow Tree Alley: 3 Madero: 3 Hukuang: 3 Nicaraguans: 3 Haitian: 3 the Chief Executive: 3 TARIFF: 3 Capital City: 3 Lusitania: 3 The Tariff Commission: 3 Atlantic City: 3 the Government of Germany: 3 Imperial German: 3 Reichstag: 3 the Labor Department: 3 this New World: 3 the Federal Reserve Banks: 3 the Declaration: 3 the Western World: 3 Fort Necessity: 3 Braddock: 3 the Continental Armies: 3 Thomas Hooker: 3 the Bureau of the Budget: 3 Ways and Means Committee: 3 the Farm Loan Board: 3 Scriptures: 3 the Ohio Company: 3 The Shipping Board: 3 The Post Office Department: 3 the Veterans of Foreign Wars: 3 Department of Education: 3 the Republican Administration: 3 Roberts: 3 Calvin Coolidge: 3 Union of South Africa: 3 the British Dominions: 3 RAILWAYS: 3 The Federal Reserve: 3 the Budget Bureau: 3 this Thanksgiving Day: 3 Merchant Marine: 3 the Senate Appropriations Committee: 3 Democratic Party: 3 the Children's Bureau: 3 Garner: 3 the Disarmament Conference: 3 the Federal Budget: 3 Federal Reserve: 3 N. R. A.: 3 the National Recovery Administration: 3 the N. R. A.: 3 Public Works: 3 White: 3 a National Government: 3 Hughes: 3 Supreme Court Justices: 3 this Western Hemisphere: 3 Easter: 3 the Civilian Conservation Corps: 3 Luxembourg: 3 the War Between: 3 the British Navy: 3 GREER: 3 United States Navy: 3 War Bonds: 3 Coventry: 3 SQUALUS: 3 John James Powers: 3 the Coral Sea: 3 Lieutenant Powers: 3 Midway: 3 the South Africans: 3 New Zealanders: 3 the Selective Service: 3 the British Commonwealth: 3 Joseph Stalin: 3 Aleutians: 3 Generalissimo: 3 the Republic of China: 3 Carl: 3 Bill of Rights: 3 Christopher Columbus: 3 the English Channel: 3 Saipan: 3 the Union of Soviet Socialist Republics: 3 the Red Army: 3 the Polish Provisional Government of National Unity: 3 the Chiefs of Staff: 3 the International Security Organization: 3 the San Francisco Conference: 3 East Prussia: 3 United Nations Organization: 3 Attlee: 3 Okinawa: 3 Commandments: 3 the Trade Agreements Act: 3 the Maritime Commission: 3 Price Control Act: 3 the Federal Housing Administration: 3 the Social Security System: 3 TVA: 3 the Office of Education: 3 Twice: 3 Barkley: 3 Guy Gillette: 3 The Economic Report: 3 B-17: 3 B-36: 3 Mount: 3 anti-Communist: 3 Dana Smith: 3 Gibson, Dunn &: 3 Crutcher: 3 Whittier: 3 Alger Hiss: 3 the National Association of Manufacturers: 3 the Government of India: 3 State Governors: 3 the Federal Aid Highway Program: 3 the Economic Report: 3 the Labor Management Relations Act: 3 the Southeast Asia Collective Defense Treaty: 3 the North Atlantic Alliance: 3 the General Agreement on Tariffs and Trade: 3 the previous Administration: 3 Executive Branch: 3 Bipartisan Commission: 3 Expediency: 3 the King of Saudi Arabia: 3 Titan: 3 the Development Loan Fund: 3 Science for Peace: 3 National Security: 3 Fortress America: 3 South Asian: 3 Protestants: 3 Vatican: 3 Sander Vanocur: 3 Frank McGee: 3 CBS: 3 Niven: 3 Batista: 3 Voice of America: 3 Spivak: 3 the Committee on Government Contracts: 3 Little Rock: 3 the Paris Conference: 3 Nationalists: 3 the Formosa Straits: 3 the Pescadores Island: 3 the Ku Klux Klan: 3 the Iraqi Government: 3 Walter Cronkite: 3 Edwards: 3 John Winthrop: 3 Food for Peace: 3 the American Society of Newspaper Editors: 3 the Committee on Ways and Means: 3 Thermopylae: 3 the International Control Commission: 3 B-52: 3 Belgrade: 3 My Nation: 3 Prince Bismarck: 3 the State of the Union Address: 3 EEC: 3 Arthur Goldberg: 3 The Common Market: 3 John McCormack: 3 Nobel: 3 McGeorge Bundy: 3 American Presidents: 3 Newton: 3 Smart: 3 the University of Alabama: 3 McCloy: 3 MacLeish: 3 Sargent Shriver: 3 de Gaulle's: 3 the University of California: 3 John McNaughton: 3 the Civil Rights Commission: 3 Owens: 3 Cormier: 3 Paul Miller: 3 The Department of Defense: 3 Latin Americans: 3 Staebler: 3 the Strategic Air Command: 3 The Joint Chiefs of Staff: 3 southeast Asia: 3 Maddox: 3 the Kingdom of Laos: 3 Russell Long: 3 Lindy Boggs: 3 Kaiser: 3 Nikita Khrushchev: 3 Dobrynin: 3 the Berlin Airlift: 3 beholden: 3 the University of Minnesota: 3 section 14(b: 3 Negro Americans: 3 the Electoral College: 3 Reedy: 3 Justice Department: 3 American Negroes: 3 Ramsey Clark: 3 Kappa: 3 the Civil Aeronautics Board: 3 Katzenbach: 3 Seamans: 3 the Inter-American Force: 3 the Negro American: 3 McDivitt: 3 Ed: 3 Great Society: 3 Marks: 3 Dear Mr.: 3 U Thant: 3 Fortas: 3 the Senate Judiciary Committee: 3 Admiral Sharp: 3 Balaguer: 3 Haiphong: 3 Associated Press: 3 the Geneva Conventions: 3 Marcos: 3 Dwight David Eisenhower: 3 the Neighborhood Youth Corps: 3 Crime Control Act: 3 Mainland China: 3 NAACP: 3 South Vietnam's: 3 Punta del Este: 3 the Saigon Government: 3 Dan: 3 Jarring: 3 VISTA: 3 the Higher Education Act: 3 the State of our Union: 3 the Fair Housing Act: 3 Albert: 3 Le Duc Tho: 3 Henry Kissinger: 3 Henry Petersen: 3
Richardson: 3 Doar: 3 the Office of Management and Budget: 3 a Nobel Prize: 3 Hutchinson: 3 John Mitchell: 3 Hunt: 3 Liddy: 3 the World Food Conference: 3 a United States of America: 3 the Clean Air Act: 3 the Vietnam War: 3 the Cambodian Government: 3 The Central Intelligence Agency: 3 the Super Dome: 3 CSCE: 3 Pacific Doctrine: 3 ASEAN: 3 the Social Security Trust Fund: 3 Susan: 3 Betty: 3 Drew: 3 Amnesty: 3 AP: 3 the Baltimore Sun: 3 James Schlesinger: 3 Sinai II: 3 the College of William: 3 Tito: 3 Yugoslavian: 3 the National Park Service: 3 the Court of Appeals: 3 the Warren Court: 3 Helms: 3 Lipshutz: 3 Department of Energy: 3 Hesburgh: 3 Sadat: 3 Edward Kennedy: 3 Ted: 3 Marvin Stone: 3 Harry Ellis: 3 William Hilliard: 3 Hilliard: 3 Ayatollah: 3 Henry Jackson: 3 the Department of Energy: 3 Republican Party: 3 Arlington National Cemetery: 3 Haig: 3 Seaga: 3 Jim Brady: 3 the House Budget Committee: 3 Carl Sandburg: 3 Pat O'Brien: 3 George Gipp: 3 Margaret Bush Wilson: 3 Benjamin Hooks: 3 Harriet Tubman: 3 Will Rogers: 3 Poli: 3 Cancun: 3 Gdansk: 3 Douglas MacArthur: 3 New Beginning: 3 The Berlin Wall: 3 Warsaw Pact: 3 Cologne: 3 the Camp David: 3 Mubarak: 3 San Bernardino: 3 the National Cathedral: 3 Constitution Avenue: 3 Herbie Petit: 3 Petit: 3 St. Peter: 3 Judeo-Christian: 3 Central America's: 3 Maurice Bishop: 3 Semper Fi: 3 the Nobel Peace Prize: 3 Sr.: 3 the Golden Gate Bridge: 3 The Census Bureau: 3 Diane: 3 Fred: 3 Closing Statements: 3 Jesse Jackson: 3 Simpson: 3 the Nobel Prize: 3 MAD: 3 Peacekeeper: 3 Pilgrims: 3 the Kennedy Center: 3 Oakland: 3 the World Series: 3 Bob Michel: 3 Independent Counsel: 3 the Brandenburg Gate: 3 Gage: 3 Southeast Asian: 3 Ann Groer: 3 Anne Groer: 3 the U.N. General Assembly: 3 Botswana: 3 O'Neil: 3 the Jefferson Memorial: 3 KGB: 3 Texas A&M University: 3 Vaclav Havel: 3 Sullivan: 3 Kurdish: 3 Schwarzkopf: 3 Patriot: 3 Scud: 3 Operation Desert Storm: 3 PAC: 3 Ross Perot: 3 Washington University: 3 Joe McCarthy: 3 the Corps of Cadets: 3 Peres: 3 Fortune 500: 3 Partnership For Peace: 3 Cold War: 3 the New Covenant: 3 the Nuclear Non-Proliferation Treaty: 3 Hope: 3 Asian-Americans: 3 Northern Ireland's: 3 Richard Dean: 3 Robin: 3 Feingold: 3 Kenny: 3 George McGovern: 3 Bethesda: 3 Hutus: 3 Tutsis: 3 Sprint: 3 the Gulf War: 3 HOPE: 3 Uganda: 3 Senegal: 3 Croatia: 3 Serb: 3 Jonesboro: 3 Slobodan Milosevic: 3 Kosovars: 3 Columbine: 3 Tommy Thompson: 3 John Breaux: 3 the USS Cole: 3 Tom Ridge: 3 Judd Gregg: 3 USA Freedom Corps: 3 Department of Homeland Security: 3 the Department of Health and Human Services: 3 Jobs: 3 Baton Rouge: 3 Al Qaida's: 3 the National Endowment for Democracy: 3 Nelson Mandela: 3 the Iraqi Governing Council: 3 the Finance Committee: 3 Ranking: 3 Kerry: 3 al-Qaeda: 3 the Author of Liberty: 3 Zarqawi: 3 Fallujah: 3 Hurricane Katrina: 3 Anbar Province: 3 Anbar: 3 Islamist: 3 Darfur: 3 General Petraeus: 3 the GI Bill: 3 IMF: 3 Chapter 11: 3 Herman: 3 Katrina: 3 AIG: 3 Harry Reid: 3 Souter: 3 American Muslims: 3 post-World War II: 3 Galesburg: 3 Anthem Blue Cross: 3 Boo: 3 Charlie Bolden: 3 Buzz: 3 Charlie: 3 the Kennedy Space Center: 3 Warren Buffett: 3 Reuters: 3 the Ninth Circuit: 3 Dorwan: 3 GPS: 3 the Space Race: 3 Tahrir Square: 3 United Kingdom: 3 Steve Jobs: 3 Charlotte: 3 Oak Creek: 3 Las Vegas: 3 Brian: 3 Bloomberg: 3 Passover: 3 Malloy: 3 Chris Murphy: 3 Warrensburg: 3 Mules: 3 Claire McCaskill: 3 THEARC: 3 Astrid: 3 Rebekah: 3 the Congressional Black Caucus: 3 Clementa Pinckney: 3 the Wright Brothers: 3 Audience members</em: 3 God Bless You: 3 MS-13 gang: 3 William Levitt: 3 the U.N. Human Rights Council: 3 Wilbur: 3 Ashlee: 3 Corey: 3 Preston: 3 Nisa: 3 Rebecca: 3 Kenton Stacy: 3 Seong: 3 Don Young: 3 Rotunda: 3 Department of Justice: 3 Jeff: 3 Narcan: 3 Azar: 3 Angel Moms: 3 Gerald: 3 Elvin: 3 U.S.-Mexico-Canada Agreement: 3 Rush Limbaugh: 3 Ann Coulter: 3 Boris Johnson: 3 Micron: 3 Venezuelans: 3 Lindsey Graham: 3 Adam Schiff: 3 Baghdadi: 3 pro-American: 3 Hispanic Americans: 3 Stephanie: 3 Sergeant Hake: 3 James Comey: 3 FISA: 3 Comey: 3 Ivanka: 3 Bobby Richardson: 3 Deborah Birx: 3 Seema: 3 OAN: 3 DPA: 3 PPP: 3 the CARES Act: 3 Abbott Labs: 3 Stanley Chera: 3 Fox News: 3 the Science and Technology Directorate: 3 Phil: 3 Antifa: 3 Christopher: 3 V.A.: 3 Angela: 3 NFL: 3 Zach Fuentes: 3 Zach: 3 Ruth Bader Ginsburg: 3 Vivian: 3 John Peter: 3 Election Night: 3 World War One: 3 Zeke: 3 Yamiche: 3 Fort Bliss: 3 Second Gentleman: 3 Affordable Care Act: 3 the Indo-Pacific: 3 the John Lewis Voting Rights Act: 3 ISIS-K: 3 Fontana: 3 Heath: 3 Danielle: 3 John Paul: 3 Weights: 2 the Post-Office and: 2 Post-Roads: 2 Militia: 2 Seamen: 2 Speech: 2 the Treaty of Fort Stanwix: 2 John Livingston: 2 Oliver Phelps: 2 Tioga: 2 Peace: 2 Men: 2 Chickamagas: 2 Authority: 2 Order: 2 the United Netherlands: 2 Executiveis: 2 Divine: 2 Allegeny: 2 Wayne: 2 the Six Nations: 2 Cherokee Indians: 2 Papers: 2 the Executive Government: 2 Situation: 2 Posts: 2 Oswego: 2 Great : 2 Article of the Treaty: 2 Manufacture: 2 Scoodiac: 2 the King of Prussia: 2 FRIENDS: 2 Samuel Bishop: 2 treasury: 2 the Naval Department: 2 Tripoline: 2 the Western Ocean: 2 the U. S.: 2 Cahokia: 2 Horn: 2 the Governments of the: 2 Piankeshaws: 2 James Monroe: 2 Iberville: 2 Alleghany: 2 Bastrop: 2 the Mississippi Territory: 2 JAMES MADISON.By: 2 An Act: 2 THE UNITED STATES OF AMERICA: 2 Fort Meigs: 2 British Province: 2 Sandusky: 2 Lake Huron: 2 Lake Champlain: 2 Decatur: 2 Bainbridge: 2 Ordnance: 2 the Illinois Territory: 2 St. Marks: 2 the King of Spain: 2 the Board of Commissioners: 2 the State of Indiana: 2 St. Peters: 2 the Pea Patch: 2 Narrows: 2 the Supreme Author of All Good: 2 New Grenada: 2 Rigolets: 2 Lake Pontchartrain: 2 Old Point Comfort: 2 Fort Washington: 2 the Board of Engineers and Naval Commissioners: 2 New States: 2 Lubeck: 2 Oldenburg: 2 His Imperial Majesty: 2 the first Comptroller of the Treasury: 2 the North West: 2 The Board of Engineers: 2 the Topographical Corps: 2 Ricarees: 2 Thompsons Island: 2 Boston Harbor: 2 the Corps of Topographical Engineers: 2 the Upper Mississippi: 2 Southern American: 2 Connecticut River: 2 Plymouth Beach: 2 arctic: 2 Swedish: 2 British America: 2 the Creek Indians: 2 Atkinson: 2 Zanesville: 2 Wilmington: 2 the Empire of Russia: 2 the Baron de Lederer: 2 the United Mexican States: 2 the Dismal Swamp: 2 Dardanelles: 2 Charlestown: 2 Gosport: 2 Poinsett: 2 Caicos: 2 McLane: 2 the Court of St. James: 2 Adrianople: 2 Bosphorus: 2 the Middle and Eastern States: 2 the State of Maryland: 2 the Constitution the States: 2 Fox Indians: 2 Treason: 2 the Constitution on the Federal Government: 2 Castle Pinckney: 2 North East: 2 the French ministry: 2 the Balearic and Canary: 2 the PostMaster General: 2 the First Congress: 2 Taney: 2 the State of New Jersey: 2 the King of the Two Sicilies: 2 Equador: 2 The French Chambers: 2 Lexington TurnPike Company: 2 The Argentine Republic: 2 The French Government: 2 Texans: 2 the Engineer Corps: 2 the Ordnance Corps: 2 The Mexican Republic: 2 their united valor: 2 the Federal authority: 2 the American States: 2 Specie: 2 Difficulties: 2 M. VAN BUREN: 2 the late Bank of the United States: 2 Menomonees: 2 Miamies: 2 the States of New York: 2 FORSYTH: 2 Chippewas: 2 the Solicitor of the Treasury: 2 the British Province: 2 the Republics of Uruguay: 2 Post-Office Departments: 2 Seminole Indians: 2 JOHN FORSYTH: 2 McLeod: 2 La Amistad: 2 Unpleasant: 2 Administrations of the Government: 2 the Fiscal Bank of the United States: 2 United States Bank: 2 the Province of Upper Canada: 2 Schlosser: 2 Palmerston: 2 the Ecuadorian Executive: 2 the Republic of Peru: 2 4th September: 2 the British West Indies: 2 Fond du Lac: 2 the Rainy Lake: 2 Pigeon River: 2 south: 2 His Majesty: 2 a European war: 2 Honeywell: 2 Monuments: 2 the Kennebec road: 2 Hog Back: 2 Leech Lake: 2 Spider River: 2 Arnolds River: 2 Richford: 2 John McCoy's: 2 Trout River: 2 Derby: 2 Quebec: 2 Montreal: 2 Volumes: 2 J. B.: 2 the Pacific: 2 exchequer: 2 These United States: 2 Cushing: 2 Chepachet: 2 Dorr's: 2 Petersburg: 2 James River: 2 Zollverein: 2 the States of Arkansas: 2 Provinces of Mexico: 2 the central Government: 2 the Government of Texas: 2 Major Snively: 2 Bryarlys Landing: 2 the States of Virginia: 2 Green: 2 The Republic of Texas: 2 Divine Being: 2 Columbia River: 2 Quadra: 2 year's: 2 valorem: 2 The Government of General Herrera: 2 the Government of General Paredes: 2 The Congress of Texas: 2 JAMES K. POLK: 2 John Ross: 2 Giver of All Good: 2 De Bocanegra: 2 the Congress of the Republic: 2 the Congress of Texas: 2 Rio Grande: 2 the Congress of that Republic: 2 the revolutionary movement: 2 the Departments of War: 2 Palo Alto: 2 the Province of New Mexico: 2 Jalapa: 2 Upper California: 2 Mexican Provinces: 2 the Papal States: 2 the Missouri Territory: 2 Hanover: 2 German States: 2 the Bay of San Francisco: 2 west coast: 2 North or: 2 Randolph: 2 Madison's: 2 Rosa: 2 Donelson: 2 Magyar: 2 the late Administration: 2 the Venezuelan Government: 2 the Seventh Census: 2 Declaration of Independence: 2 the supreme law of the land: 2 Rio Colorado: 2 Constitutionand: 2 Governmentto: 2 the Arctic Seas: 2 the Department for the fiscal year: 2 Congress of March 3, 1821: 2 Spaniard: 2 Armstrong: 2 N. H.: 2 The Naval Academy: 2 Hanover county: 2 the world respectably: 2 the Kentucky Legislature: 2 the Kentucky House of Representatives: 2 the Legislature of Kentucky: 2 Union of these States: 2 Otis: 2 North-West: 2 the North Western Territory: 2 North West: 2 South Americans: 2 Saint Voltaire: 2 Siamese: 2 the American Colonization Society: 2 Ruler: 2 Pharaoh: 2 the Northern Pacific: 2 Behrings Straits: 2 the States of Europe: 2 Constitution of the United States: 2 trustI: 2 the Empire of China: 2 Intelligence: 2 the Secretaries of War and Navy: 2 the Territories of Utah: 2 Franklin Pierce: 2 the Territories of Oregon: 2 Chagres: 2 Republic of Central America: 2 the States of Nicaragua: 2 the Nicaragua Transit Company: 2 the Black Warrior: 2 the State of Kentucky: 2 Giddings: 2 Virginian: 2 the Virginia Legislature: 2 Thenceforward: 2 it:“The Missouri Compromise: 2 Compromise: 2 disturb.”I: 2 David Wilmot: 2 The “Wilmot: 2 the Illinois Legislature: 2 the Resolutions of the Illinois Legislature: 2 WHOLLY: 2 the “slave-dealers&rdquo: 2 Our Declaration of Independence: 2 the States of Nebraska: 2 South Carolinian: 2 EQUAL: 2 keepers?I: 2 GREAT: 2 it.”It: 2 FRONT: 2 ASPECT: 2 Missourians: 2 WITHOUT: 2 Union?The: 2 COMPROMISE: 2 &c: 2 WOULD: 2 AMERICAN CITIZENS: 2 Pettit: 2 the Declaration of Independence “a: 2 forty: 2 Nebraska Senators: 2 Southerners: 2 pieces&rdquo: 2 INTENDED: 2 EFFECT: 2 south east: 2 the “Divine: 2 Nebraska “perfectly free&rdquo: 2 EXPRESS: 2 WHOLE: 2 H.R.: 2 Charles Allen: 2 Richard Yates: 2 Phillips: 2 Lo: 2 Gentiles: 2 the Pugets Sound Agricultural Company: 2 the States of that Republic: 2 the Transit Company: 2 the States or Territories of the Union: 2 the Baltic Sea: 2 El Dorado: 2 the Mexican States: 2 Lower California: 2 the Hawaiian Kingdom: 2 the house of representatives: 2 the States of California: 2 the Territories of Washington: 2 theAmerican: 2 Republic of Honduras: 2 Panama Railroad: 2 the Water Witch: 2 the Paraguayan Government: 2 Geary: 2 the State of Kansas: 2 Van Vliet: 2 the Government Congress: 2 Topeka State: 2 Lane: 2 Robinson: 2 Justice Taney: 2 Fort Bridger: 2 Salt Lake City: 2 Chihuahua: 2 Virgin Bay: 2 the Republic of: 2 Republic of Mexico: 2 the States of the Confederacy: 2 Tien-tsin: 2 Assumption: 2 the Republics of Honduras: 2 Dalles: 2 Pickett: 2 Comonfort: 2 Miramort: 2 Marquez: 2 Tepic: 2 the Governments of Costa Rica: 2 Thomas Mifflin: 2 Hugh Williamson: 2 William Blount: 2 the Territorial Act: 2 Jonathan Dayton: 2 Charles Pinckney: 2 Gouverneur Morris: 2 John Rutledge: 2 Black Republicanism: 2 a Congressional Slave-Code: 2 Slavery: 2 Popular Sovereignty: 2 a Republican party: 2 Orsini: 2 John Browns: 2 the National Territories: 2 these Free States: 2 Disunionists: 2 END: 2 DARE TO DO OUR DUTY AS WE: 2 Covode: 2 Nero: 2 Wildfire: 2 Fernando J. Moreno: 2 Avenue: 2 The Southern States: 2 the States of the North: 2 Fuchau: 2 Tycoon: 2 Charleston Harbor: 2 National Union: 2 the Cumberland Gap: 2 the Secretaries of State: 2 Accomac: 2
General McClellan: 2 the Russian Empire: 2 cuttings: 2 the Congress of the nation: 2 St. Charles: 2 James C. Conkling: 2 the Capital of Illinois: 2 Meade: 2 republican party: 2 New-England: 2 Key-Stone: 2 Jersey: 2 The Sunny South: 2 Antietam, Murfreesboro: 2 Sam: 2 muddy bayou: 2 Britannic Majesty's Government: 2 the Sierra Nevada: 2 the Washington Aqueduct: 2 The Agricultural Department: 2 Jacob Thompson: 2 Beverley Tucker: 2 George N. Sanders: 2 William C. Cleary: 2 W. HUNTER: 2 John Letcher: 2 William Smith: 2 Ninth: 2 Eleventh: 2 Twelfth: 2 Abandoned Lands: 2 Americanpeople: 2 A Proclamation Whereas: 2 the Senators and Representatives: 2 League Island: 2 Counsel: 2 the State of Colorado: 2 the house of representatives of: 2 supreme authority: 2 Congress of the United States: 2 Judicial: 2 the North German Union: 2 The Republic of Mexico: 2 the Republics of Hayti: 2 Parana: 2 St. Thomas: 2 the King of Denmark: 2 the British Crown: 2 Edwin M. Stanton: 2 U. S. Grant: 2 U. S.: 2 Stanton's: 2 Chronicle: 2 Yours: 2 Griffin: 2 Kans.: 2 the "Act: 2 Article III: 2 the Republics of Peru: 2 article 3: 2 the Republic of the United States of Colombia: 2 St. George: 2 the Agricultural Bureau: 2 ARTICLE XV: 2 the Republic of San Domingo: 2 The Government of San Domingo: 2 the States of America: 2 Spanish American: 2 Porto: 2 Danish West: 2 Dutch West Indies: 2 the North German Confederation: 2 Saxony: 2 Edward Thornton: 2 Rhine: 2 the Departments of Government: 2 Bureaus: 2 Chester: 2 Laurens: 2 Newberry: 2 Chesterfield: 2 the Austro-Hungarian Empire: 2 Catacazy: 2 the Giver of All Good: 2 Bancroft: 2 the Haro Channel: 2 Her Majesty's Government: 2 the Republics of Central: 2 Rock Island: 2 INTERIOR DEPARTMENT: 2 The Ninth Census: 2 Chief Executive: 2 Yara: 2 Departments of Government: 2 the Pay Department: 2 William P. Kellogg: 2 Warmoth: 2 the Federal judiciary: 2 McEnery: 2 Register: 2 Radical: 2 the White League: 2 Montijo: 2 The Government of Spain: 2 The Court of Commissioners of Alabama Claims: 2 the Subsistence Department: 2 the Sioux Reservation: 2 Yrujo: 2 Fillmore: 2 Pierce: 2 Commissary Generals: 2 James B. Eads: 2 the Centennial Exhibition: 2 the International Exhibition of: 2 Rutherford B. Hayes: 2 The Government of Italy: 2 the Army Regulations: 2 their National Government: 2 the Regents of the Smithsonian Institution: 2 the standard gold dollar: 2 the Postal Union: 2 Hot Springs: 2 the National Museum: 2 VI: 2 the Representatives of the people: 2 No Department of the Government: 2 St. Pierre: 2 The Government of China: 2 Apache: 2 the Pay Corps: 2 unsettle: 2 the Imperial Government of China: 2 were&mdash: 2 The Universal Postal Union: 2 Pa.: 2 Oreg: 2 Sitting Bull: 2 Comptroller: 2 Believing: 2 section 5: 2 Masonic: 2 the King of the Belgians: 2 The International Sanitary Conference: 2 the National Board of Health: 2 Cowboys: 2 theExecutive: 2 the Library of Congress: 2 the Republics of South America: 2 CHESTER A. ARTHUR: 2 JAMES G. BLAINE: 2 Steamships: 2 the American Isthmus: 2 Oregon War: 2 De Long: 2 George W. De Long: 2 the Light-House Service: 2 District Commissioners: 2 Chileans: 2 the Spanish West Indies: 2 Crook: 2 the International Association of the Kongo: 2 Tangier: 2 The Siamese Government: 2 the Gun Foundry Board: 2 United States Army: 2 Thetis: 2 Winfield S. Schley: 2 the Straits of Magellan: 2 Callao: 2 Keiley: 2 Mongolian: 2 the Korean Government: 2 Santos: 2 the Haytian Government: 2 the Latin Union: 2 Andrew J. Hill: 2 Liberty Enlightening the World: 2 the Board on Fortifications: 2 Federal or State: 2 the Freedman's Savings and Trust Company: 2 the Committee on Pensions: 2 United States Civil Service Commission: 2 messenger: 2 shall-- : 2 Postal Rule II: 2 Large: 2 Frederick: 2 Saloman: 2 the Board of Ordnance and Fortifications: 2 Apache Indians: 2 Mount Vernon Barracks: 2 Vesuvius: 2 Petrel: 2 American Navy: 2 theStates: 2 the New England States: 2 the Susquehanna River: 2 Williamsport: 2 Congress of July 9, 1888: 2 the Government of Nicaragua the Interoceanic Canal Company: 2 the San Juan River: 2 Costa Rican: 2 the Government of Belgium: 2 the Persian Government: 2 Point Barrow: 2 David S. Terry: 2 Lathtop, Cal.: 2 Terry: 2 the bureaus of the Department: 2 Vandalia: 2 Nipsic: 2 Charles Foster: 2 William Warner: 2 George Crook: 2 section 14: 2 Lucius Fairchild: 2 Alfred M. Wilson: 2 Warren G. Sayre: 2 Section 12: 2 the Seminole Nation: 2 Merritt: 2 the Department of the: 2 Rudolph Hering: 2 Samuel M. Gray: 2 Frederick P. Stearns: 2 Lyman: 2 the Official Register: 2 section 13: 2 the Sioux Nation: 2 The International Marine Conference: 2 the Kongo Basin: 2 the Republic of Salvador: 2 Ezeta: 2 San Jose de Guatemala: 2 Champerico: 2 The Nicaragua Canal: 2 the Governments of Mexico: 2 Edmund Monson: 2 the Meridian Conference: 2 the Government of Portugal: 2 John Ericsson: 2 The Customs Administration Board: 2 the "National Guard: 2 Sisseton: 2 Wahpeton: 2 Pottawatomies: 2 Absentee Shawnees: 2 non-Mormon: 2 Wilford Woodruff: 2 Woodruff: 2 the States of Wyoming: 2 The Oceanic Steamship Company: 2 Auckland: 2 Auckland to San Francisco: 2 The South Atlantic: 2 Arthur: 2 supreme power: 2 Fonseca: 2 Iquique: 2 Rear-Admiral Brown: 2 Sail Diego: 2 Government of Chile: 2 the Government of Balmaceda: 2 Article VIII: 2 the Kongo State: 2 Blair: 2 The International Boundary Commission: 2 King Kalakaua: 2 Queen Liliuokolani: 2 Title VII: 2 the Navassa Phosphate Company: 2 section 5576: 2 the coined dollars: 2 Proctor: 2 Watervliet the Army: 2 Ainsworth: 2 the Court of Private Land Claims: 2 the North German Lloyd: 2 the Pacific islands: 2 The Brazil Steamship Company: 2 the Division of the: 2 the Territory of Oklahoma: 2 the Cherokee Commission: 2 Tonkawa: 2 the Cherokees of their claim: 2 a Court of Private Land Claims: 2 the World's Columbian Exposition: 2 King's: 2 Egan's: 2 the diplomatic corps: 2 Jenkins: 2 Mare Island: 2 the Chilean navy: 2 Calderon: 2 South Atlantic: 2 the Rocky Mountain States: 2 the Tenth Census: 2 A. B. Shepperson: 2 the New York Cotton Exchange: 2 Ira Ayer: 2 the State of Massachusetts: 2 Bessemer steel: 2 Liverpool 10,941,800 tons: 2 the St. Marys Canal: 2 Fall River: 2 The Society of the Unemployed: 2 Eastport: 2 St. Marys Falls Canal: 2 Joseph Nimmo: 2 the Senate Select Committee on Relations with Canada: 2 the Falls of Niagara: 2 the King of Italy: 2 the San Pedro River: 2 Bourdour: 2 Konia: 2 Asia Minor: 2 the Postmaster-General for eleven mail routes: 2 Boulogne: 2 the Canadian Pacific Railway: 2 American Pacific: 2 The Court of Private Land Claims: 2 the organized civil community: 2 Cheyenne: 2 Arapahoe Indians: 2 the Choctaws and Chickasaws of the lands: 2 Kiowas: 2 Comanches: 2 Kiowa: 2 Comanche Reservation: 2 Greet County: 2 the Grand Review: 2 Agricultural: 2 Queen Liliuokalani: 2 the Queen's Government: 2 Unless Government: 2 Malietoa Laupepa: 2 King of Samoa: 2 James H. Blount: 2 Blount: 2 the Department of Texas: 2 Chickamauga: 2 Machias: 2 Castine: 2 the Government of Hawaii: 2 the Belgian Government: 2 the Paris Tribunal: 2 the Mosquito Indian Strip: 2 Bluefields: 2 Salvadorean: 2 Aana: 2 Tamasese: 2 the Universal Postal Union: 2 the International Navigation Company: 2 the Census Office: 2 Comptroller of the Currency: 2 A Proclamation Whereas the island: 2 Tamatav: 2 the Kiel Canal: 2 Mount St. Elias: 2 Yukon: 2 Erzerum: 2 the Life-Saving Service: 2 the Revenue-Cutter Service: 2 the Steamboat-Inspection Service: 2 the Bureau of Navigation: 2 Post-Office Department: 2 Newport News: 2 the Central Pacific Railroad: 2 The Weather Bureau: 2 provides--: 2 Lynchings: 2 Zanjon: 2 Sagasta: 2 Hawaiians: 2 Tribal: 2 the Marine Hospital Service: 2 The Union Pacific Railway: 2 the United States Court: 2 the Civil Service: 2 the American National Red Cross: 2 Weyler: 2 Blanco: 2 WILLIAM: 2 the Madrid Government: 2 the Naval Militia: 2 Merrill: 2 San Juan de Puerto Rico: 2 Santiago Harbor: 2 Merrimac: 2 Sampson: 2 Clara Barton: 2 the Duke of Almodovar: 2 Ladrones: 2 George Gray: 2 the Atacama Desert: 2 Lattimer: 2 the Universal Exposition: 2 The Nicaragua Canal Commission: 2 John G. Walker: 2 the Paris Exposition: 2 Handy: 2 the Afognak Forest: 2 section 24: 2 The Dawes Commission: 2 the National Congress: 2 the National Banking Act: 2 the Departments of the Government: 2 Pretoria: 2 the General Act: 2 the Governments of Germany: 2 Tutuila: 2 Article VI: 2 Article VII of the Treaty of Peace: 2 Anglo: 2 the International Union of American Republics: 2 the Philadelphia Commercial Museum: 2 The Grand Army: 2 Lake Tahoe: 2 the University of Michigan: 2 the Naval Observatory: 2 Yamen: 2 Liscurn: 2 the united forces: 2 Marroquin: 2 San Clemente: 2 Restraint: 2 Luke I. Wright: 2 Henry C. Ide: 2 Bernard Moses: 2 Mindanao: 2 Census: 2 the Civil Service Board: 2 the Hall of the House of Representatives: 2 Honesty: 2 Pekin: 2 pan-American: 2 McKinley's: 2 Doubt: 2 the Bureau of Forestry: 2 Hague: 2 Battle: 2 The Hague Court: 2 ARTICLE I. : 2 ARTICLE II: 2 Article I and: 2 ARTICLE V. : 2 ARTICLE VI: 2 ARTICLE VII: 2 ARTICLE VIII: 2 ARTICLE IX: 2 ARTICLE X. : 2 ARTICLE XI: 2 THEODORE ROOSEVELT DEPARTMENT OF STATE: 2 CORRESPONDENCE BETWEEN THE DEPARTMENT OF STATE: 2 Keep Department: 2 Communicate: 2 Federico Boyd: 2 the Department of Panama: 2 Royal Mail: 2 Bunau Varilla: 2 Boyd: 2 Amador: 2 Bocas del Toro: 2 Senor Philippe: 2 Foreign Relations: 2 Eagle: 2 article 35: 2 EMBASSY OF THE UNITED STATES: 2 Recognition: 2 Andes: 2 Kingston: 2 Republic of Panama: 2 the Stikine River: 2 the Hague: 2 Gifford Pinchot: 2 the Bureau of Forestry of the Department of Agriculture: 2 the War College: 2 the General Board of the Navy: 2 Seward: 2 Everett: 2 the Panama Railway: 2 gram: 2 L'Enfant: 2 bison: 2 Yosemite: 2 Maker: 2 Thanksgiving Day: 2 the Gulf of Alaska: 2 the Yukon River: 2 the Interparliamentary Union: 2 Macedonia: 2 American Republic: 2 the Hospital Corps: 2 Scandinavian: 2 the Copyright Office: 2 the Yosemite National Park: 2 the Grand Canyon: 2 the Canadian Government: 2 the Philippine Government: 2 John F. Stevens: 2 Consulate: 2 Consulates: 2 the Isthmian Commission: 2 Neill: 2 Tuskegee: 2 the Joint Traffic Association: 2 the Middle Ages: 2 Magoon: 2 the Bureau of American Republics: 2 the Third Conference at Rio: 2 every American Republic: 2 the Tribunal of Paris: 2 the Red Cross Conference: 2 the Governments of Russia: 2 The United States Navy: 2 Ancon: 2 Mallet: 2 Ripley: 2 Bierd: 2 the French Company: 2 W. C. Gorgas: 2 Shouts: 2 Garlington: 2 Nettleton: 2 Federal board or commission: 2 the Great Plains: 2 Australasia: 2 Board of Visitors: 2 the interstate commerce law: 2 Appalachians: 2 Eastern Asia: 2 Meyer: 2 Central Asia: 2 PHILIPPINES: 2 RICO: 2 JAPANESE: 2 The American Government: 2 the organized militia: 2 FederalGovernment: 2 Schedule L: 2 Cotton: 2 the Far West: 2 the Appropriations Committee: 2 the Spitzbergen Islands: 2 Spitzbergen: 2 THE NEAR EAST: 2 The Government of Chile: 2 Central American Republics: 2 The International Bureau of American Republics: 2 Executive Order: 2 REVENUES: 2 the Spooner Act: 2 the American Sugar Refining Company: 2 the Hepburn Act: 2 the Court of Commerce: 2 the interstate Commerce Commission: 2 Monopoly: 2 Knight: 2 the Sherman Act: 2 THE FAR EAST: 2 the Hukuang Loan: 2 Pan American: 2 the Civil Service Act: 2 the Bureau of Engraving: 2 Army Engineers: 2 the Army Engineers: 2 the Coast Artillery: 2 NEED: 2 General Staff: 2 the Court of Appeals of the District of Columbia: 2 The American Bar Association: 2 the Circuit Court of Appeals: 2 Treasury Departments: 2 the postal savings bank: 2 the North Pole: 2 RECLAMATION: 2 the Zoological Park: 2 Fort Myer: 2 the Supreme Court of the District: 2 Gillett: 2 Yale University: 2 the Standard Oil Trust: 2 Standard Oil: 2 the American Tobacco Company: 2 the Porto Rican Tobacco Company: 2 VOLUNTARY: 2 the Powder Trust: 2 the Department of justice: 2 George V: 2 compositeur: 2 Infantry: 2 Mexican Government: 2 The Pan American Union: 2 Frederick the Great: 2 the Foreign Affairs Committee: 2 EFFICIENCY: 2 National Reserve Association: 2 Army Service Corps: 2 the Staff Corps: 2 the General Staff Corps: 2 Illinois River: 2 Ill.: 2 the Republics of Central America: 2 Tacna-Arica: 2 Guayaquil: 2 Habana: 2 united effort: 2 the reserve association: 2 the War College Division: 2 beriberi: 2 the Atlantic Fleet: 2 The Interior Department: 2 the Panama-Pacific International Exposition: 2 Valor: 2 Victoriano Huerta: 2 the Iturbide Bridge: 2 boat's: 2 Huertista: 2 Mayo: 2 Independence Square: 2 the American Declaration of Independence: 2 the European war: 2 Imperial German Government: 2 Sussex: 2 The Imperial German Government: 2 James, Gentlemen: 2 the National Democratic Convention: 2 Boasting: 2 a Tariff Board: 2 a Trade Commission: 2 the Rural Credits Act: 2 Warehouse Act: 2 the Federal Department of Labor: 2 the Progressive Party: 2 The Trade Commission: 2 the Bureau of Foreign and Domestic Commerce: 2 Davis: 2 the Eastern Mediterranean: 2 Housatonic: 2 Fry: 2 Palermo: 2 Reserve Corps: 2 the County Clerk: 2 Northern France: 2 Brest-Litovsk: 2 Central Empires: 2 the War Trade Board: 2 the Food Administration: 2 the National Committee: 2 Middle West: 2 Henry Cabot: 2 Stars and Stripes: 2 the Democrat Party: 2 the Transportation Act: 2 Envy: 2 this Capital City: 2 The Labor Board: 2 the Permanent Court of International Justice: 2 the Mixed Claims Commission: 2 Post: 2 WELFARE: 2 Our National Government: 2 HIGHWAYS: 2 the Cape Cod Canal: 2 SHIPPING BOARD: 2 Regulars: 2 Virginians: 2 the French War: 2 the Massachusetts Bay Colony: 2 American Nation: 2 George III: 2 Arthur Young: 2 Thomas Shepherd: 2 the Veterans' Bureau: 2 the Comptroller General: 2 Locarno: 2 League: 2 our Supreme Court: 2 the Alien Property Custodian: 2 Chamber of Commerce: 2 Hooker: 2 The Inland Waterways Corporation: 2 Leonard Wood: 2 the Grand Army: 2 National Capital: 2 Patrick Henry: 2 War Finance Corporation: 2 The American Red Cross: 2 INLAND: 2 the American: 2 Fort Sackville: 2 Henry L. Stimson: 2 Samuel Gompers: 2 Program: 2 Paramaribo: 2 the Postal Service: 2 the Governments: 2 Croats and Slovenes: 2 Aristide Briand: 2 Frank B. Kellogg: 2 M. Briand: 2 Borah: 2 Swanson: 2 the German Reich: 2 the Republic of Poland: 2 the Czechoslovak Republic: 2 AGRICULTURE: 2 the Radio Commission: 2 VETERANS : 2 the Law Enforcement Commission: 2 The Payne-Aldrich: 2 Underwood: 2 the Japanese Navy: 2 The Reserve System: 2 Nation's: 2 National, State: 2 Cordell Hull: 2 Arthur Vandenberg: 2 the Kellogg-Briand Pact: 2 the National Credit Corporation: 2 the Senate Finance Committee: 2 the Democratic House: 2 Power Commission: 2 that&mdash: 2 the Hawley-Smoot: 2 the States of Arizona: 2 The Democratic House of Representatives: 2 George Washington's: 2 Democratic House of Representatives: 2 Governorship: 2 Better Homes: 2 Home Ownership: 2 The Surgeon General: 2 Farm Loan Banks: 2 the Farm Credit Administration: 2 Southeast: 2 Gothic: 2 the National Industrial Recovery Act: 2 Division: 2 Dante: 2 the United States Employment Service: 2 the Employment Service: 2 WPA: 2 Wagner: 2 American Government: 2 our National Government: 2 Preamble: 2 AAA: 2 Justice Stone: 2 Federal Judges: 2 Federal Court: 2 Federal Courts: 2 the Works Progress Administration: 2 Seventy-Fifth Congress: 2 Bulkley: 2 the Northwest Ordinance: 2 American Hemisphere: 2 Sabbath: 2 Plymouth Rock: 2 Legislative: 2 Inauguration: 2 Magna Charta: 2 the Pan American Union: 2 Austrians: 2 Norwegians: 2 the French Army: 2 Antarctic: 2 the Nazi Government: 2 Wake Island: 2 Midway Island: 2 the Dutch Indies: 2 China Sea: 2 Bataan Peninsula: 2 the Netherlands East Indies: 2 Fleet: 2 Lend-Lease: 2 Russian Army: 2 the Burma Road: 2 Flying Fortress: 2 Japs: 2 the Medal of Honor: 2 the Solomon Islands: 2 Australians: 2 the Selective Service System: 2 mid-Pacific: 2 Rotterdam: 2 The War Labor Board: 2 Coal: 2 Cunningham: 2 United Nations': 2 the Third War Loan: 2 Solomons: 2 Pacific Oceans: 2 Santa Claus: 2 the English: 2 the Ural Mountains: 2 Roman Empire: 2 Oceans: 2 Emerson: 2 GERMANY: 2 the Nazi Party: 2 Curzon: 2 Marshal Tito: 2 Red Army: 2 Sevastopol: 2 the Curzon Line: 2 The Crimea Conference: 2 the Pacific war: 2 Ibn Saud: 2 Tragic: 2 Oak Ridge: 2 Richland: 2 State Byrnes: 2 Davies: 2 Pauley: 2 the Council of Foreign Ministers: 2 this Council of Foreign Ministers: 2 Leyte Gulf: 2 Tarawa: 2 the Federal Program: 2 The United Nations Organization: 2 the International Bank for Reconstruction and Development: 2 the Selective Service Act: 2 Civilian: 2 the Smaller War Plants Corporation: 2 Federal and Federally: 2 Social Security System: 2 Savings: 2 Federal Budget: 2 the War Shipping Administration: 2 the Servicemen's Readjustment Act: 2 the Bretton Woods Agreements: 2 the International Bank: 2 a Temporary Joint Commission: 2 ATOMIC ENERGY: 2 a single Department of National Defense: 2 The Greek Government: 2 the Wagner Act: 2 Morse: 2 the National Association for the Advancement of Colored People: 2 Cooperatives: 2 the International Trade Organization: 2 the Banking and Currency Committee: 2 anti-Catholic: 2 this United States: 2 Forrest Smith: 2 the St. Lawrence Seaway: 2 the Columbia Valley Administration: 2 the Communist Party: 2 North Atlantic Treaty: 2 North Koreans: 2 the North Koreans: 2 Exodus: 2 Teapot Dome: 2 World War III: 2 Meet the Press: 2 Price: 2 G.I.: 2 Sparkman: 2 Hiss: 2 Franklin Roosevelt's: 2 Taft-Hartley: 2 the West Wing: 2 Early: 2 Rhineland: 2 the World War II: 2 Argonne: 2 the Seventh Fleet: 2 the Federal Bureau of Investigation: 2 the Hoover Commission: 2 The Food and Drug Administration: 2 the Sea of Japan: 2 united Germany: 2 East European: 2 Hammarskjold: 2 the United States': 2 Eastern Germany: 2 the Associated States: 2 the Department of the Treasury: 2 Psalmist: 2 Advisory Committee: 2 the Executive Office: 2 Federal-State: 2 Heads of Government: 2 the North Atlantic Treaty Organization and Western European Union: 2 Federal German Republic: 2 Atomic Energy Conference: 2 an International Agency: 2 the United Nations Subcommittee on Disarmament: 2 Mutual Security Program: 2 our Foreign Service: 2 The Rural Development Program: 2 The Great Plains Program: 2 the Colorado River Storage Project: 2 National Parks: 2 Parks: 2 Assistance: 2 Federal technical and: 2 the Organization of the Executive Branch: 2 Commissions: 2 the White House Conference on Education: 2 Longshoremen: 2 Harbor Workers': 2 the District of Columbia Unemployment Insurance Law: 2 the National Housing Act: 2 Urban Planning: 2 the Federal National Mortgage Association: 2 Veterans Pensions: 2 Insular Affairs: 2 the Refugee Relief Act: 2 vista: 2 the Republican Party's: 2 the Armed Services: 2 Creator: 2 Fidelity: 2 a Cabinet Committee: 2 Organization for Trade Cooperation: 2 Next: 2 Eastward: 2 the Seat of Government: 2 the Mid East: 2 85th Cong: 2 the 85th: 2 Strategic Air Command: 2 the Korean Armistice: 2 Arias: 2 the Rule of Law: 2 the North Polar Sea: 2 the Committee on Recent Social Trends: 2 Robin Hood: 2 the Employment Act: 2 Price Stability for Economic Growth: 2 The International Monetary Fund: 2 The McClellan Committee: 2 the Congressional Record of: 2 Antarctica: 2 Western Nations: 2 the Organization for European Economic Cooperation: 2 The Civil Rights Commission: 2 Congressional-Executive: 2 Fellow Democrats: 2 Tory: 2 Unitarian: 2 Bowie: 2 Crockett: 2 Bailey: 2 the Democratic Party's: 2 Mutual News: 2 Fleming: 2 the Labor Committee: 2 NOVINS: 2 WARREN: 2 the House Rules Committee: 2 Javits: 2 Kerr: 2 Landon: 2 Edward P. Morgan: 2 Title Three: 2 Gavin: 2 post-war: 2 George Aiken: 2 Bill Shadel: 2 Shadel: 2 Yarnell: 2 Radford: 2 the Geneva Conference: 2 Budapest: 2 Adam Clayton Powell: 2 George Allen: 2 Gallup Polls: 2 Quincy Howe: 2 General Twining: 2 the Inter-American: 2 Sahara: 2 Frank Singiser: 2 John Edwards: 2 John Chancellor: 2 EDWARDS: 2 Cronkite: 2 Iron Curtain: 2 Robertson: 2 Pilgrim: 2 Arbella: 2 Courage: 2 South Viet Nam: 2 the Arctic Circle: 2 the Organization for Economic Cooperation and Development: 2 Udall: 2 Ribicoff: 2 Jose Figueres: 2 Service: 2 George William Curtis: 2 Leonidas: 2 John Milton: 2 the North Atlantic Community: 2 the European Continent: 2 Macaulay: 2 Civil Defense: 2 the Arc de Triomphe: 2 Schonbrunn Palace: 2
the Queen of England: 2 anti-American: 2 Queen Elizabeth: 2 Prince Philip: 2 East German: 2 Bastogne: 2 B-47: 2 Army Divisions: 2 Air National Guard: 2 the UN Charter: 2 Hodges: 2 Bruce Catton: 2 Antietam: 2 Odegaard: 2 the Morrill Act: 2 West Berliners: 2 I'll: 2 Sino-Soviet: 2 the Export-Import Bank's: 2 Meany: 2 Bryant: 2 Housing: 2 Atlantic Community: 2 the European Common Market: 2 Harkins: 2 Griswold: 2 William F. Buckley: 2 the National Aeronautics and Space Administration: 2 the University of Mississippi: 2 the State of Mississippi: 2 Meredith: 2 the United States Court of Appeals: 2 the Tennessee Valley Authority: 2 Aristotle: 2 the Foreign Service: 2 LeMay: 2 Doolittle: 2 the Great Republic: 2 West New Guinea: 2 National Service Corps: 2 Alabama National Guardsmen: 2 Ich bin ein Berliner: 2 Bernard Baruch: 2 General Assemblies: 2 Kashmir: 2 Rawlings: 2 Amherst: 2 Cole: 2 the Food for Peace: 2 Thanksgiving: 2 the Atlantic Missile Range: 2 the NASA Launch Operation Center: 2 the John F. Kennedy Space Center: 2 Farris Bryant: 2 Cape Kennedy: 2 the Lower Colorado: 2 All United Nations: 2 french: 2 Redeye: 2 Walleye: 2 Shriver: 2 medicare: 2 Lopez Mateos: 2 William P. Bundy: 2 Eastern Affairs: 2 Defense for International Security Affairs: 2 Daniel M. Luevano: 2 Luevano: 2 Clark Kerr: 2 Frankie Muse: 2 Associate General Counsel: 2 the St. Louis Housing and Land Clearance Authority: 2 Several A-11: 2 Edwards Air force Base: 2 Edwards Air: 2 The Lockheed Aircraft Corporation: 2 Burbank: 2 Whitney Aircraft Division: 2 the United Aircraft Corporation: 2 the Hughes Aircraft Company: 2 Osborne: 2 the South Viet-Nam: 2 the Pathet Lao: 2 George Ball: 2 Paul Findley: 2 the Securities and Exchange Commission: 2 GS-12: 2 the Committee for the Preservation of the White House: 2 George Brown: 2 Committee for the Preservation of the White House: 2 the Chamber of Commerce: 2 the Economic Council: 2 The Presidential Scholars: 2 Johns Hopkins University: 2 Krag: 2 FCC: 2 Rhodes Scholars: 2 the Southwest Texas State Teachers College: 2 Once Al Smith: 2 the Old Testament: 2 Douglas-Home: 2 U235: 2 William Bundy: 2 the International Coffee Agreement: 2 the Departments of State and Defense: 2 Prophecy: 2 von Hassel: 2 the Federal Republic of Germany's: 2 the Federal Mediation and Conciliation Service: 2 Booker T. Washington: 2 U.S.-Panamanian: 2 SR-71: 2 the Inter-American Committee on the Alliance for Progress: 2 the Security Council of the United Nations: 2 Newhouse: 2 the Democratic Party of 1964: 2 Long: 2 Gillis Long: 2 Luci: 2 Ellender: 2 the Full Employment Act: 2 Tommy: 2 Takla: 2 Makan: 2 Central Asian: 2 The British Labor Party: 2 Conservatives: 2 Stanford University: 2 Rice County: 2 Civilian Conservation Corps: 2 Norman Thomas: 2 a Department of Housing and Urban Development: 2 Hickenlooper: 2 National Security Affairs: 2 the Council of Economic Advisers: 2 Ala.: 2 John Doar: 2 Job Corps: 2 the Golden Gate: 2 the Alabama National Guard: 2 Burke Marshall: 2 Maxwell Field: 2 Craig: 2 Administrator Webb: 2 Jim Webb: 2 John Young: 2 Brewster: 2 Tydings: 2 Viet-Nam's: 2 he!p South Viet-Nam: 2 Mekong River: 2 Ph.D.: 2 Legal Adviser: 2 the Job Corps: 2 Juan Bosch: 2 Trujillo: 2 Black: 2 Inter-American Force: 2 the Peace Committee: 2 Hotel: 2 Dryden: 2 Major White: 2 dangers--: 2 this International Cooperation Year: 2 Leonard Marks: 2 the Disarmament Committee: 2 Rowan: 2 the Office of President: 2 Appomattox: 2 the Federal Register: 2 The Air Force: 2 Eugene P. Foley: 2 Associate Special Counsel: 2 Bress: 2 a Great Society: 2 the Teacher Corps: 2 Stalinist: 2 Wendell Willkie: 2 William C. Westmoreland: 2 Dillon: 2 first Inaugural Address: 2 the Foreign Service Institute: 2 Sorensen: 2 Komer: 2 7th Circuit: 2 Rostow: 2 Hindus: 2 Moslems: 2 -Japan: 2 Detroit News: 2 Sarah McClendon: 2 the Constituent Assembly: 2 Llewellyn Thompson: 2 Ellsworth Bunker: 2 Methodist: 2 Fowler: 2 Dawson: 2 Times: 2 Fogarty: 2 SUCCESS: 2 Department of Business and Labor: 2 Selective Service System: 2 The National Commission on Selective Service: 2 Brandeis: 2 the Law Enforcement Assistance Act: 2 the National Crime Commission: 2 Congress the Safe Streets: 2 War Labor Board: 2 The Federal Home Loan Bank Board: 2 this Great Society: 2 The Asia of tomorrow: 2 East Asian: 2 David Lilienthal: 2 Robert Komer: 2 Andrew Jackson's: 2 Lilienthal: 2 the Government of Vietnam: 2 The National Guard: 2 Sarah: 2 the Senate Armed Services Committee: 2 Mills: 2 a SEATO Treaty: 2 Rather: 2 Fisher: 2 the International Development Association: 2 the Asian Development Bank: 2 all?--but: 2 the Wholesome Meat Act: 2 the Product Safety Commission: 2 Consumer Affairs: 2 National Capital of Washington: 2 Drug Control Act: 2 the Juvenile Delinquency Act: 2 the Highway Beautification Act: 2 North Vietnam's: 2 Daley: 2 the Vietnamese war: 2 the Apollo 8: 2 the Commission on Executive: 2 Legislative and: 2 Judicial Salaries: 2 the Kappel Commission: 2 Mike Mansfield: 2 Pro Tem: 2 Richard Brevard Russell: 2 State Rogers: 2 Japanese-American: 2 Phnom Penh: 2 Cambodians: 2 Parrot's Beak: 2 Karl Taylor: 2 Karl: 2 Xuan Thuy: 2 McGovern: 2 the Watergate affair: 2 the Re-Election Committee: 2 Elliot Richardson: 2 Godgiven: 2 the White House Counsel: 2 the Middle Eastern: 2 White House Counsel: 2 Jaworski: 2 Alexander Solzhenitsyn: 2 the Agriculture Department: 2 Rodino: 2 the Council on Wage: 2 Federal Reserve Board: 2 the Outer Continental Shelf: 2 Eddie Hebert: 2 Dave Treen: 2 the Battle of New Orleans: 2 Finnish: 2 the Federal Republic of Germany: 2 North-South: 2 the Fourth of July: 2 Anger: 2 Edwin Newman: 2 the Walnut Street Theatre: 2 Humphrey-Hawkins: 2 the Transportation Department: 2 Vail: 2 the Federal Energy Agency: 2 anti-Washington: 2 Burns: 2 the National Security Agency: 2 the Palace of Fine Arts Theatre: 2 Secretaries of State: 2 Radio Free Europe: 2 Saudis: 2 Jerry Brown: 2 UAW: 2 Carla Hills: 2 Small Business Administration: 2 Justice Burger: 2 Circuit Court of Appeals: 2 Jerry Ford: 2 Andrew Young: 2 Helen Thomas: 2 the U.N. Human Rights Commission: 2 the South Korean Government: 2 the Department of Housing and Urban Development: 2 LEAA: 2 the Golan Heights: 2 Begin: 2 OSHA: 2 the Shanghai Communiqué: 2 Sino-American: 2 Christmases: 2 the Straits of Hormuz: 2 Cruise: 2 Harry: 2 Tel Aviv: 2 Business Week: 2 the South Bronx: 2 WALTERS: 2 U.N. 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BANCROFT DAVIS: 1 the Swiss Confederation: 1 the States of the Church: 1 the Kingdom of Italy: 1 the Grand Duke: 1 Mikado: 1 the Venezuelan Claims Commission: 1 ocean steamers—and: 1 the Post-Office Department of the Government: 1 Kuklux: 1 the Committee to Investigate Southern Outrages: 1 Saints: 1 Department of the public service: 1 The Advisory Board: 1 Naval Solicitor: 1 Solicitor of Internal Revenue: 1 Register of the Treasury: 1 Second Comptrollers: 1 Majesty's Government: 1 Great Britain': 1 the Governments of Her Britannic Majesty: 1 Territory of Alaska: 1 the Imperial Parliament: 1 the International Statistical Congress: 1 The Centennial Celebration: 1 the Vienna Exposition: 1 Trieste: 1 Republics of the Pacific: 1 miscellaneous412,254.71Total: 1 arsenals35,372,157.20For naval establishments: 1 navy-yards21,249,809.99For: 1 revenue42,958,329.08For: 1 account$99,960,003.54Reduction: 1 the Medical and Surgical History of the War: 1 Congress of July 28, 1866: 1 La Crosse: 1 Sutro Tunnel: 1 the Signal Office: 1 the Academy: 1 S.C.: 1 Ga.: 1 Kanawha: 1 James River Canal: 1 the Department for the fiscal year ending June 30, 1872: 1 The Pacific Mail Steamship Company: 1 Brazil Mail Steamship Company: 1 Mexican Steamship Company: 1 the Pacific Mail Steamship Company: 1 Land Office: 1 the Committee on Patents: 1 Congress of June 8, 1872: 1 the War of 1812 36,551: 1 CENSUS: 1 Heretofore Federal: 1 Fellow-Citizens: Under Providence: 1 the International Exhibition of the Products of Agriculture: 1 Shenandoah: 1 the Dutch Government: 1 The Venezuelan Government: 1 the Republic of Santo Domingo: 1 the Claims Commission: 1 the Orange Free State: 1 Tornado: 1 Committee on Privileges and Elections: 1 The State of New York: 1 The State of Illinois: 1 the St. Clair Flats: 1 the Postmaster-General for the establishment: 1 Englebrecht: 1 the Civil-Service Board: 1 Congress of June 17, 1874: 1 Empires: 1 the Governments of China: 1 Simonosaki: 1 the Court of Commissioners of Alabama Claims: 1 the Government of Turkey: 1 Mexican Joint Claims Commission: 1 D. B. Penn: 1 Article 13: 1 Congress the State: 1 Centennial: 1 Atlantic Seaboard: 1 Alexandria Railroad: 1 Congress of June 20, 1874: 1 Congress of June 20,: 1 the State of Louisiana: 1 Section 15: 1 Antoine: 1 the Committee on Privileges and Elections of the Senate: 1 Misinformed: 1 Cruikshank: 1 Levi Nelson: 1 Hadnot: 1 Nash: 1 Cazabat: 1 Shaw: 1 Colfax: 1 Coushatta: 1 T. S. Crawford: 1 P. H. Harris: 1 Representations: 1 New Orleans Barracks: 1 The White Leagues: 1 the Shreveport Campaign Club: 1 People's: 1 the White Leagues: 1 Ku Klux Klans: 1 the "House: 1 the Mississippi Valley States: 1 pagan: 1 the General Cortes of Portugal: 1 Good Return: 1 the King of the Hawaiian Islands: 1 the United States Direct Cable Company: 1 the Department for the last year: 1 the Signal-Service Corps: 1 the Territories of Colorado: 1 the War: 1 the Post-Office Departments: 1 the Commission of Food Fishes: 1 The Bureau of Agriculture: 1 the Territories of Wyoming: 1 Declare: 1 Oliver Wolcott: 1 Germantown: 1 John Breckenridge: 1 the United States Mint: 1 Yrujo's: 1 Bushrod Washington: 1 Montpelier: 1 Duane: 1 Amos Kendall: 1 the Provinces of Mexico: 1 A Proclamation The centennial anniversary of the day: 1 Montana Territories: 1 Article XXXIV: 1 1842.The Ottoman Government: 1 Congress of March 23, 1874: 1 Own: 1 the Territory of Colorado: 1 the Sioux Indian Nation: 1 the working bureaus of the Department: 1 the united opposition of one: 1 Flour: 1 the Constitution on Congress: 1 Fellow-Citizens: We: 1 North andSouth: 1 Departments of our Government: 1 the united voice and: 1 the supreme judiciary: 1 Divine Hand: 1 R.B.: 1 the State of West Virginia: 1 Martinsburg: 1 Ohio Railroad: 1 R. B. HAYES: 1 F. W. SEWARD: 1 the national conventions: 1 the Constitution on the President: 1 the Turkish Governments: 1 the States of the Empire of Germany: 1 The Fishery Commission: 1 Articles XVIII: 1 XXV: 1 Porfirio Diaz: 1 the Republics of Central and South America: 1 the States of West Virginia: 1 Ord: 1 the Department for the fiscal year ending June 30, 1877: 1 this Department for the advancement of agricultural industry: 1 the Government in exchange: 1 the Lower Mississippi: 1 The States of Louisiana: 1 The Universal Exposition: 1 the sinking fund: 1 recommends--1: 1 Henry H. Benner: 1 Eighteenth Infantry: 1 J.M. Chambers: 1 purpose.4: 1 International Postal Congress: 1 Berne October 9, 1874: 1 this Universal Postal Convention: 1 the Postal Convention: 1 Bannocks: 1 Northern Cheyennes: 1 the States of Kansas: 1 the Red Cloud Sioux: 1 the council of September, 1877: 1 the Hampton Normal Agricultural Institute: 1 the Hot Springs Commission: 1 The Reform School: 1 this Government of Articles V: 1 Anson Burlingame: 1 customs: 1 declares--: 1 considered--: 1 section 5529: 1 the Commons of England: 1 Section 4 of Article: 1 Constitution declares--: 1 Sections 2005: 1 Section 2022: 1 Section 2024: 1 Section 5522: 1 Lords: 1 section 2027: 1 Article 1: 1 State of New York: 1 the Fortieth Congress: 1 Title 26: 1 Executives: 1 the "Civil Service Commission: 1 the Government of Egypt: 1 the Egyptian Government: 1 the Lew Chew Islands: 1 the Central American Governments: 1 the Quartermaster-General's Office: 1 the Department on June 30, 1879: 1 the White River Ute Reservation: 1 the Mescalero Agency: 1 the Hampton Normal: 1 Agricultural Institute: 1 the Washington National Monument: 1 Eastern Branch: 1 Representatives or Delegates in Congress: 1 Ex parte Siebold: 1 National Governments: 1 the United States America: 1 section 1767: 1 Mormonism: 1 Senate—a: 1 The International Commission: 1 International Exhibition of Fish and Fisheries: 1 the Governments of Austria-Hungary: 1 the Empire of Morocco: 1 Trescot: 1 the Kingdom of the Netherlands: 1 Bucharest: 1 Prince Charles: 1 Republics of Peru: 1 Samoa the Government of King Malietoa: 1 Oceanica: 1 Farman: 1 J. M. Batchelder: 1 redemption&mdash: 1 coin silver dollars: 1 Chiriqui Lagoon: 1 the Bay of Golfito: 1 no Department of the Government: 1 the Eastern Cherokees: 1 Southern Apaches: 1 Lafayette Square: 1 the Congressional Library: 1 the Articles of Confederation and Perpetual Union: 1 the supreme authority of government: 1 proclaiming"liberty: 1 the Constitution of 1787: 1 Suicide: 1 The Territories of the UnitedStates: 1 Prosperity: 1 the Fortune Bay: 1 Aspee Bay: 1 British Indians: 1 FrenchRepublic: 1 the Bimetallic Conference: 1 the Electrical Exhibition: 1 Alsace: 1 Lorraine: 1 Rhenish: 1 Baron von Steuben: 1 AmericanClaims Commission: 1 the Czar Alexander II: 1 American Israelites: 1 the Geographical Congress of Venice: 1 BeneficenceCongress: 1 the Hygienic Congress of Turin: 1 Justin W. Parsons: 1 The Swiss Government: 1 hisGovernment: 1 The Costa Rican Government: 1 ArgentineConfederation: 1 theEuropean: 1 the proposedPanama canal: 1 andBrazil: 1 theBrazilian: 1 Patagonian: 1 these Republics of the: 1 andGovernment: 1 theWestern: 1 Pacific power: 1 Yokohama of: 1 The King of Hawaii: 1 Governments of: 1 the Southern Pacific: 1 the Geneva convention: 1 the Atlanta Cotton Exposition: 1 navy-yards : 1 3,400.00 : 1 manufactures;$21,462,534.34: 1 and$6,469,643.04: 1 Fort: 1 the SubsistenceDepartment of the Army: 1 the Engineer Battalion: 1 theSouth Pass of the Mississippi River: 1 thePotomac River: 1 AdvisoryBoard: 1 CostaRica: 1 chapter 36: 1 chapter 39,applied: 1 Hampton, Carlisle: 1 Western Territories: 1 theDistrict: 1 theWashington Monument: 1 the words 'regulate: 1 Article I of which--: 1 Monarchy: 1 The States of the Pacific Slope: 1 Caucasian: 1 Pacific Slope: 1 The State of California: 1 the States of North and South America: 1 the kindred States: 1 mutatis: 1 States of Central and South America: 1 the Governments of America: 1 section 12: 1 The Italian Government: 1 the Cantons of Switzerland: 1 The Ottoman Porte: 1 Port au Prince: 1 the Government of Hayti: 1 Kingdom of Servia: 1 J. B. Eads 304,000 Unexpended: 1 the Arctic Ocean: 1 George W. Melville: 1 the Lena River: 1 Chipp: 1 Surgeon James M. Ambler: 1 Jerome J. Collins: 1 Hans Halmer Erichsen: 1 Heinrich H. Kaacke: 1 George W. Boyd: 1 Walter Lee: 1 Adolph Dressier: 1 Carl A. Gortz: 1 Nelse Iverson: 1 Ah Sam: 1 Charles W. Chipp: 1 William Dunbar: 1 Alfred Sweetman: 1 Waiter Sharvell: 1 Albert C. Kuehne: 1 Edward Star: 1 Henry D. Warren: 1 Peter E. Johnson: 1 Giles B. Harber: 1 Willliam H. Scheutze: 1 Congress of March 3, 1881: 1 Robert M. Berry: 1 Lieutenant Berry: 1 Engineer Melville's: 1 Charles F. Putnam: 1 Cape Serdze: 1 Parrott: 1 the late Naval Advisory Board: 1 no United States: 1 conveyed-- Cents : 1 Danubian: 1 the Franco-American Claims Commission: 1 the British Islands: 1 the national church: 1 The Helvetian Confederation: 1 Sofia: 1 Bulgarian: 1 Benjamin Well: 1 the Abra Silver Mining Company: 1 Mexican Claims Commission: 1 the King of Tab Chosen: 1
Chosenese: 1 Simonoseki: 1 Monrovia this Government: 1 the Mannah River: 1 the International African Association: 1 Kongo Valley: 1 navy-yards15,283,437.17For: 1 the District of Columbia3,817,028.48For: 1 fund46,556.96Of: 1 cent65,380,250.00Of: 1 PacificRailway: 1 etc298,696.78901,303.22From: 1 sources1,237,189.632,382,810.37Total: 1 arsenals13,512,204.3326,487,795.67For naval establishment: 1 the District of Columbia1,138,836.412,611,163.59For: 1 section 3141: 1 The Naval Advisory Board: 1 "The Utah Commission: 1 the National Legislature of the: 1 pleuropneumonia: 1 Saskatchewan: 1 The Industrial Exhibition: 1 the Southern Exposition: 1 The Centennial Exposition: 1 Chester A. Arthur: 1 FREDK: 1 the Kongo Valley: 1 The Government of Korea: 1 the Siamese Government: 1 The Government of Japan: 1 Liberty Enlightening: 1 The Government of Hawaii: 1 the Spanish Crown: 1 the Spanish Indies: 1 The International Meridian Conference: 1 the Governments of England: 1 An International Inventions Exhibition: 1 pensions55,429,228.06For: 1 navy-yards17,292,601.44For: 1 revenue43,939,710.00For: 1 manufactures53,842,292Silk: 1 Compliance: 1 the Ordnance Bureau: 1 the Surgeon-General's Office: 1 the Naval Advisory Board: 1 Hotchkiss: 1 Lieutenants Harber: 1 the Lena Delta: 1 Yakutsk: 1 Lady Franklin Bay: 1 Bear: 1 George W. Coffin: 1 William H. Emory: 1 Upernavik: 1 Melville Bay: 1 Littleton Island: 1 Cape Sabine: 1 Governors Island: 1 the Utah Commission: 1 the Pacific end: 1 the San Juan: 1 Engineer Corps: 1 Federalcontrol: 1 the Federal Governmentand: 1 Federal;and: 1 Jefferson-- "Peace: 1 havinga due: 1 Thomas A. Hendricks: 1 The Argentine Government: 1 The Austro-Hungarian Government: 1 humani generis: 1 The Colombian Government: 1 Engineer Menocal: 1 His Majesty the King of the Belgians: 1 Julio R. Santos: 1 American Claims Commission: 1 the War of Secession: 1 the Governments of Spain: 1 British American: 1 Pelletier: 1 Lazare: 1 Port-au-Prince: 1 the Government of Peru: 1 the Spanish Antilies: 1 section 3659: 1 Federal naturalization: 1 North and Central America: 1 Bermudas: 1 The Governments of Belgium: 1 Industrial Exposition: 1 the congress of the Latin Union: 1 1878.The Steamboat-Inspection Service: 1 The Supervising Surgeon-General: 1 the Coast and Geodetic Survey: 1 the Lieutenant-General: 1 The Board on Fortifications: 1 the Department of contract: 1 the Department of five and a quarter million dollars: 1 Postal Union: 1 The Indian Bureau: 1 Chiricahua Apaches: 1 the White Mountain Reservation: 1 Crow Creek: 1 the Polar Sea: 1 the Civil Service Commissioners: 1 Federal court of a duty: 1 Bureau of Labor: 1 Government the Bureau: 1 claimant's: 1 Alfred J. Hill: 1 the Franco-American Union: 1 Joseph W. Drexel: 1 the Heads of Departments: 1 Her Britannic Majesty's Government: 1 Polynesian Island: 1 the Dark Continent: 1 A. K. Cutting: 1 the Shah's Government: 1 Governments-Germany: 1 King Malietoa: 1 the Government of the Queen Regent: 1 the Caracas Commission: 1 the Internal-Revenue Bureau: 1 Geronimo: 1 Fort Marion: 1 the Land Department: 1 Labor Bureau: 1 the War of 1812 576,622: 1 Rebellion: 1 hereafter--: 1 the House Committee on Pensions: 1 the War of the Rebellion every Northern State: 1 the Government the Government: 1 Delegates in Congress: 1 the Congress of the United States: You: 1 the duty thereon: 1 REVISED CIVIL-SERVICE RULES: 1 The State,: 1 section 1754 of the Revised Statutes: 1 the State, Territory: 1 Prescribe: 1 I. Persons: 1 section 163: 1 DEPARTMENTAL RULE II: 1 Copyist: 1 the Commission of each application: 1 --These: 1 -These: 1 I. A: 1 Department the Commission: 1 Commission--: 1 Departmental Rule II: 1 Inspectress: 1 Examiner: 1 Sampler: 1 the classified customs service shall: 1 examiner: 1 Customs Rule II: 1 POSTAL: 1 POSTAL RULE II: 1 Carrier: 1 Messenger: 1 examinations.--These: 1 Cashier: 1 Document O: 1 Article I of the treaty: 1 Ikisima: 1 The Empire of Brazil: 1 "The Board of Ordnance and Fortifications: 1 the Watervliet Arsenal: 1 Herreshoff: 1 the Department for the three years ending June 30, 1888,: 1 New England StatesMiddle: 1 41)1,406The States and Territories of the Pacific: 1 the Money-Order Division: 1 British Honduras: 1 the Postmaster-General for their classification: 1 the Perpetual Emigrating Fund Company: 1 the Church of Jesus Christ: 1 Latter-day: 1 the Turtle Mountain Indians: 1 Crees: 1 the States of New Jersey: 1 Governmentthe: 1 Congressand: 1 Washingtonand: 1 Atlanticseaboard: 1 The Territory of Dakota: 1 individualto: 1 Europeanstatesmen: 1 Republicfor: 1 Noneare: 1 communities?I: 1 Stateshitherto: 1 benefitsof: 1 any European Government: 1 no European Government: 1 American States: 1 exclusivelyAmerican: 1 Ahigh: 1 the Union of the Dakotas: 1 Washington Territories: 1 TheNational Congress: 1 theState: 1 perpetuateit: 1 juggler: 1 A Proclamation A hundred years: 1 Jacksonville: 1 Pursuant: 1 School: 1 the Conference of American States: 1 the Territory of Dakota: 1 the Keya Paha River: 1 the State of Nebraska,': 1 George P. Litchfield: 1 James E. Helms: 1 Department of the Interior: 1 Ponca Agency: 1 section 26: 1 section 25: 1 ALVEY A. ADEE: 1 Coeur d'Alene: 1 The Conference of Brussels: 1 Coeur: 1 William Windore: 1 Folger: 1 day': 1 Ocean: 1 the Mare Island Navy-Yard: 1 The Sisters of Charity: 1 La Patria: 1 Yankees: 1 the Calle del Arsenal: 1 the Plaza Wheelright: 1 Senor Mazzini: 1 Seaman Davidson: 1 McCrea: 1 Captain Schley: 1 Surgeon Stitt: 1 Placilla: 1 Admiral Brown: 1 Quinteros: 1 The La Patria: 1 Charles Langen: 1 Wharton: 1 Patrick Shields: 1 Shields: 1 Peteira: 1 Pereira: 1 the Republican national convention: 1 departments &mdash: 1 a Democratic House of Representatives: 1 the civil war: 1 the sea &mdash: 1 Elaine: 1 the Associated Chambers of Commerce of Great Britain: 1 The British Trade Journal: 1 the Democratic national convention: 1 the bureau of labor: 1 the Industrial World: 1 Swansea: 1 Ayer: 1 the per cent: 1 Kolb: 1 Agricultural Departments: 1 the Nicaragua Ship Canal: 1 the Committees of Foreign Affairs: 1 development—of: 1 BEJ: 1 37,285.The Textile World: 1 Tracy: 1 Government?Lawlessness: 1 The Textile World: 1 Efforts: 1 Contracts: 1 Lawlessness: 1 Lorin A. Thurston: 1 W. R. Castle: 1 W. C. Wilder: 1 C. L. Carlet: 1 Joseph Marsden: 1 kindred: 1 Federal power the General Government: 1 Americanmanliness: 1 Andwhen: 1 Sung-pu: 1 Article XII: 1 the Bering Sea Tribunal of Arbitration: 1 Amapala: 1 the wanton act: 1 The Government of Honduras: 1 Cavally: 1 the Liberian Legislature and Executive: 1 Paso del Norte: 1 King Malietoa's: 1 Santa Maria: 1 the Columbian Exposition: 1 Anatolia College: 1 Marsovan: 1 Turkish Koordistan: 1 the Venezuelan Transportation Company: 1 the Provisional Government of the islands: 1 the International American Conference of: 1 The Superintendent of Immigration: 1 the Army Gun Factory: 1 Watervliet: 1 Chickamauga and Chattanooga National Military Park: 1 Robert Williams: 1 Railway Mail Service: 1 Montgomery and Marblehead: 1 Katahdin: 1 Olympia: 1 Indian Government: 1 such Provisional Government: 1 the Committee of Foreign Affairs: 1 Hawaiian Government: 1 Blount's: 1 the Queen of Hawaii: 1 Queen's: 1 the de jure Government: 1 Skerrett: 1 the Government of the islands: 1 de fato: 1 the committee of annexation: 1 Doubts: 1 W. Q. GRESHAM: 1 the Antwerp Industrial Exposition: 1 Da Gama: 1 Emilio Santos: 1 Articles V: 1 Governments in Bering Sea: 1 North Pacific Ocean: 1 of$425,000: 1 the Island Empire: 1 The Japanese Empire: 1 Leon Baldwin: 1 Durango: 1 Rama: 1 Arguello: 1 Krzeminski: 1 The Government of Salvador: 1 Melton: 1 Special: 1 Samoans: 1 Upolu: 1 Atuans: 1 the Department of the East: 1 Fort Sill: 1 Ind. T.: 1 John M. Schofield: 1 the General Commanding the Army: 1 the Union Pacific Railway Company: 1 The Postmaster-General renews the suggestion: 1 the Washington Navy-Yard: 1 Benham: 1 Coast Survey: 1 the Hydrographic Office of the Navy Department: 1 Cheyennes: 1 Arapahoes: 1 army: 1 the War of 1812.The: 1 tuberculin: 1 D. E. Salmon: 1 The Office of Experiment Stations: 1 the United States Department of Agriculture: 1 the Department for the present fiscal year: 1 the national board: 1 John D. Kernan: 1 Nicholas E. Worthington: 1 D. Wright: 1 coin reserve: 1 section 3700: 1 the Emperor's Government: 1 The Governments: 1 John L. Waller: 1 Hovas: 1 the Bering Sea Arbitration Tribunal: 1 the Maritime Conference of Washington: 1 Prince of Wales Island: 1 the Frozen Ocean: 1 the United States Coast and Geodetic Survey: 1 the Royalist party: 1 Thurston: 1 the Mosquito Nation: 1 the Mosquito Strip: 1 Hatch: 1 Corinto: 1 The Russian Government: 1 Allianca: 1 Colon to New York: 1 Antonio Maximo Mora: 1 Sivas: 1 non-Mussulman: 1 Scutari: 1 Erzerum and Harpoot: 1 the Solicitor of the Department of State: 1 the Consular Bureau: 1 Julian Pauncefote: 1 the Governments of the Old World: 1 Asiatic Turkey: 1 Harpoot and Marash: 1 the Northern Pacific and Bering Sea: 1 Cree Indians: 1 the Fort Leavenworth Military Prison: 1 the Dead-Letter Office: 1 cents93.0From: 1 the Bureau of Construction and Repair: 1 the Bureau of Supplies and Accounts: 1 The War College: 1 skeleton crews on board: 1 The Naval Militia: 1 non-Indian: 1 the Western Pacific Railroad Company: 1 Central Pacific: 1 the Departments of Treasury: 1 the Union Pacific Company: 1 Profoundly: 1 United Statesfor: 1 andAmerican: 1 Americanname: 1 Americanmerchant: 1 AmericanNavy: 1 Arbitration: 1 the British Houseof Commons: 1 Executivehimself: 1 Iconsider: 1 Presidentto: 1 Consul: 1 Martinez de Campos: 1 the Spanish Cortes: 1 the Hawaiian State: 1 William L. Merry: 1 W. Godfrey Hunter: 1 the Governments of Guatemala: 1 Edward O. Wolcott: 1 Adlai E. Stevenson: 1 Charles J. Paine: 1 John A. Kasson: 1 the Governments of Japan: 1 The Government of Great Britain: 1 the Universal Exposition of 1900: 1 the Yukon Valley: 1 Randall: 1 the Eighth Infantry: 1 St. Michael: 1 Yukon River: 1 Dyea: 1 the Indian Government: 1 the Tribal Governments and Courts: 1 the Tribal Governments: 1 section 16: 1 Seminole Nations: 1 the Choctaws: 1 tribes-- the Choctaws and Chickasaws: 1 the Creek Nation: 1 the National Council of said Nation: 1 the Indian Appropriation Act: 1 the Tribal Nations: 1 Henry L. Dawes: 1 the United States Courts: 1 Main Line: 1 the Kansas Pacific Line: 1 lien: 1 the Cuban war: 1 the Province of Pinar del Rio: 1 Santa Clara: 1 cultivation&rdquo: 1 Canovas: 1 Woodford’s: 1 Grant’s: 1 Jackson’s: 1 the naval court of inquiry on the destruction: 1 the Americas Navy: 1 Peninsula: 1 the Queen Regent: 1 A.D.: 1 succor: 1 the naval board of inquiry: 1 united American: 1 effect.|| Due: 1 the Declaration of Paris: 1 the Life-Saving: 1 Light-House: 1 The Signal Corps: 1 Caimanera: 1 George Dewey: 1 Mirs Bay: 1 Major-General Merritt: 1 Winslow: 1 Worth Bagley: 1 Antillean: 1 the North Atlantic Squadron: 1 Major-General Shafter: 1 Daiquiri: 1 Tenth Cavalry: 1 the First United States: 1 Volunteer Cavalry: 1 the 2d El Caney: 1 the Maria Teresa: 1 Almirante Oquendo: 1 Cristobal Colon: 1 Santiago Spain's: 1 Camara: 1 Shafter: 1 Higginson: 1 Schwan: 1 the Third Army Corps: 1 General Brooke: 1 Guayama: 1 Hormigueros: 1 Coamo: 1 Yauco: 1 Cape San Juan: 1 Puerto Rican: 1 Admiral Dewey's: 1 I. Spain: 1 James F. Wade: 1 William T. Sampson: 1 Matthew C. Butler: 1 John R. Brooke: 1 Brigadier-General: 1 William W. Gordon: 1 The Puerto Rican Joint Commission: 1 The Cuban Joint Commission: 1 William R. Day: 1 Cushman K. Davis: 1 William P. Frye: 1 Whitelaw Reid: 1 Andean: 1 Magellan Straits: 1 the Argentine Government: 1 Thomas Jefferson Page: 1 the Congress of Argentina: 1 the Empress-Queen: 1 Luzerne County: 1 the Greater Republic: 1 the United States of Central America: 1 United States of Central America: 1 Kiao: 1 Wei-hai-wei: 1 Port Arthur: 1 Talienwan: 1 Szechuen: 1 Kutien: 1 the Ozama River: 1 Moses P. Handy: 1 Thomas W. Cridler: 1 Ferdinand W. Peck: 1 section 4 of the same: 1 the Imperial Government of the: 1 Swiss Governments: 1 Ramsden: 1 The Government of Greece: 1 Bernard Campbell: 1 Shelby M. Cullom: 1 John T. Morgan: 1 Robert R. Hitt: 1 Sanford B. Dole: 1 Walter F. Frear: 1 Jesus Guerra: 1 Guerra: 1 Report No: 1 the Treasury Departments: 1 The Mexican Water Boundary Commission: 1 Bancos: 1 Victor H. McCord: 1 Hans: 1 Article I of the general act: 1 Jaluit: 1 Nicholas John Hannen: 1 the International Red Cross: 1 Article IV: 1 Fuller: 1 Justice Brewer: 1 Herschell: 1 Richard Collins: 1 M. F. De Martens: 1 Felipe Scandella: 1 Volunteer Army: 1 Kansas Pacific: 1 the Union Pacific: 1 the Kansas Pacific Railroad: 1 the Central Pacific Railroad Company: 1 The Supervising Architect: 1 the Pine Mountain: 1 Zaca Lake Reserve: 1 the Prescott Reserve: 1 the Pecos River Reserve: 1 the Fish-Culture Reserve: 1 The Forestry Division of the Department: 1 Hobart: 1 section 3694: 1 The Industrial Commission: 1 the Congress of June 18, 1898: 1 the Trans-Missouri Freight Association: 1 Traffic Association: 1 Pando: 1 non-Catholic: 1 Bolivian: 1 the Brazilian Legislature: 1 The Panama Star: 1 the Nicaraguan Canal Commission: 1 Republic of Santo Domingo: 1 Heureaux: 1 Juan Isidro Jimenez: 1 Eluefields: 1 the Mosquito Territory: 1 The Maritime Canal Company: 1 U. S. N.: 1 the Atrato River: 1 the Chilean Senate: 1 Nanking: 1 Tsing-tao: 1 Kiao chao: 1 Ta-lien-wan: 1 z898: 1 American National Institute: 1 the Congresses of Public Charity: 1 Borkum Emden: 1 the North Sea: 1 High Commission: 1 Articles III: 1 the American Commissioners: 1 the British Commissioners: 1 the Boer States of Africa: 1 Orange Free State Governments: 1 XIXth Article: 1 Morgan City: 1 Kobe: 1 the Pacific Cable Company: 1 Mattie Rich: 1 the Mexican Executive: 1 The International Water Boundary Commission: 1 The Hague Disarmament Conference: 1 Malietoa Tanu: 1 Pago Pago: 1 Ofoo: 1 Olosenga: 1 Manua: 1 the Duke of Arcos: 1 Madrid Hon: 1 Bellamy Storer: 1 The International Commission of Arbitration: 1 Barima Point: 1 Andrade: 1 Barbados,: 1 the Republics of South and Central America: 1 the Republics of America: 1 International American Conference: 1 Governments of the Latin: 1 Pan-Amencan Exposition: 1 Erie: 1 the States of the United States: 1 National Museum: 1 the United States Commission: 1 International Commercial Congress: 1 The Hague on the 18th of May: 1 Andrew D. White: 1 Seth Low: 1 Stanford Newel: 1 Alfred T. Mahan: 1 William Crozier: 1 Frederick W. Holls: 1 the Geneva Convention of 1864: 1 The Convention for the Pacific Settlement of International Conflicts: 1 the Convention for the Pacific Settlement of International Conflicts: 1 the Congress of April 22: 1 Director-General of Posts: 1 Post-Office Inspector: 1 Assistant Postmaster-General: 1 Pacific Railroad: 1 cash$58,448,223.75Kansas Pacific: 1 secured47,050,172.36Kansas Pacific: 1 Fish Culture Reserve: 1 the Trabuco Canyon: 1 the Mount Rainier Reserve: 1 the San Francisco Mountains: 1 the Black Mesa: 1 Gallatin: 1 Gila River: 1 Fish Lake: 1 the far East: 1 Iloilo: 1 Jacob Gould Schurman: 1 Cornell University: 1 Charles Denby: 1 San Pedro: 1 the Major-General Commanding the Department: 1 Bacolod: 1 Visayan: 1 the Sulu Islands: 1 J. C. Bates: 1 United States Volunteers: 1 Article I the sovereignty of: 1 Article X: 1 General Bates: 1 Tagal: 1 the Constitution of the United: 1 the Hawaiian Republic: 1 "The Territory of Hawaii: 1 the Government of many millions of dollars: 1 Immediate: 1 Taiping: 1 the Yang-Tse: 1 Throne: 1 Palace: 1 The Tsung-li Yamen: 1 the Pei-Ho: 1 Langfang: 1 Baron von Ketteler: 1 Claude MacDonald: 1 E. G. Squiers: 1 Captain Myers: 1 Jung Lu: 1 Chang Yen-hoon: 1 Wu Ting-fang: 1 General Chaffee: 1 Yangtsun: 1 Ho-si-woo: 1 Tung Chow: 1 Chi-li: 1 Earl Li: 1 Hung Chang: 1 Prince Ching: 1 Rockhill: 1 the International Convention of June, 1899: 1 the Commission of 1893: 1 the Chilean Congress: 1 E. P. Alexander: 1 Jimenez: 1 Ozama: 1 Seine: 1 the Lafayette Monument: 1 Great Britain's: 1 Delagoa Bay: 1 Russo: 1 R. H. May: 1 the Guatemalan Government: 1 George F. B. Jenner: 1 Frank H. 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Porter: 1 national naval reserve: 1 the Olympic Reserve: 1 The Prescott Reserve: 1 the Big Horn Reserve: 1 Santa Ynez: 1 the Crow Creek Forest Reserve: 1 section 7: 1 Chapter 34: 1 the Civil Laws of Hawaii: 1 the Bureau of trained: 1 The United States Civil Service Commission: 1 the United States Philippine Commission: 1 John Marshall Day: 1 the Capital of the Republic: 1 the Corcoran Gallery of Art: 1 Treasuryreceipts: 1 thesum: 1 the Constitution of the United States"and: 1 Governmentin: 1 restupon: 1 Governmentdeems: 1 Theprinciples: 1 Congresshas: 1 Governmentfor: 1 PhilippineIslands: 1 Byfar: 1 Milburn: 1 A Proclamation: 1 Pan-American Exposition at Buffalo: 1 Judas: 1 King Humbert: 1 gulf: 1 the Interstate-Commerce Act: 1 Commerce and Industries: 1 the Interstate Commerce Law: 1 Ships: 1 The Act of March 14, 1900: 1 the National Banking Law: 1 the Interstate Commerce Act: 1 Lax: 1 Queen of the Antilles: 1 South Atlantic States: 1 Clayton-Bulwer: 1 Alcibiades and Hamilcar: 1 Tromp and Blake: 1 the Spanish Navy: 1 supreme command: 1 This General Board: 1 a National Naval Reserve: 1 the Civil Service Law: 1 man's: 1 The General Allotment Act: 1 the St. Louis Exposition: 1 the Louisiana Purchase: 1 the Charleston Exposition: 1 The Pan-American Exposition at Buffalo: 1 the National Zoological Park: 1 William Woodville: 1 the Shanghai River: 1 the Pan-American Congress: 1 La Abra: 1 Queen Victoria: 1 Frederick of: 1 at': 1 Platt: 1 American Continent: 1 the French Panama Canal Company: 1 Pacific-: 1 the Commercial Pacific Cable Company: 1 the U. S. S. Nero: 1 trans-Pacific: 1 company's: 1 Brest: 1 the Mohammedan Moros: 1 the Admiral of the Navy: 1 The isthmian canal: 1 the Naval School: 1 the Commission of Fish and Fisheries: 1 smith work: 1 Museum: 1 Commercial Convention: 1 Houses of the Congress of the United States: 1 the Reciprocal Commercial Convention: 1 Tasker H. Bliss: 1 Carlos de Zaldo: 1 Beurmann: 1 Honorable: 1 M. Garcia: 1 Montes: 1 Article I and being the: 1 the Customs Tariff: 1 ARTICLE IV. : 1 Schedule A.: 1 Paragraphs 21 and: 1 Schedule B.: 1 Butter: 1 hemp: 1 Group 2: 1 Schedule C: 1 Paragraphs 197 and 198: 1 pasteboard: 1 manufactures thereof: 1 Paragraphs 157 to: 1 Paragraph 105: 1 Schedule C. To: 1 TASKER H. BLISS: 1 CARLOS DE ZALDO: 1 JOSE M. GARCIA MONTES: 1 the isthmian canal: 1 Barranquilla: 1 Jose Augustin Arango: 1 Tomas Arias: 1 Cables Nashville: 1 Padilla: 1 Sent 12.51 p.m.: 1 Filippe Bunau Varilla: 1 Manavi: 1 the Committee of the Government: 1 Amador Guerrero: 1 Arosemena: 1 Gunboat Bogota: 1 Sent 4 p.m.: 1 morning's: 1 Colombians: 1 Colon and Monkey Hill: 1 Vessel: 1 Tranquillity: 1 Porfirio Melendez: 1 a. m. English: 1 THE PANAMA GOVERNMENT: 1 PRESIDENTE DE LOS ESTADOS UNIDOS: 1 Francisco V. de la Espriella: 1 J. M. ARANGO: 1 NEW YORK: 1 the Mother of the American Nations: 1 Beaupre: 1 Cauca: 1 Colombian Government: 1 a. m. I: 1 Bedronel Ospina: 1 Lucas Cabellero: 1 Lorenzo Marroquin: 1 INCLOSURE: 1 EXCELLENCY: 1 the Colombian Department of Panama: 1 THOMAS HERRAN: 1 TOWER: 1 THE PRESIDENT'S REPLY TO THE REMARKS: 1 Minister: 1 Very Respectfully: 1 WILLIAM H. MOODY: 1 American Consul: 1 Colon, Porto Bello: 1 armed force: 1 Ancon Hill: 1 CRUISER ATLANTA: 1 AMERICAN CONSUL: 1 Gunboat: 1 BOSTON: 1 MOODY NAVY DEPARTMENT: 1 Porto Bello: 1 Woods Hole: 1 Mass.: 1 the State of Cauca: 1 St. Andrews Island: 1 Receipt: 1 Amaya: 1 Morales: 1 Cape Mala: 1 Atlas Line's: 1 R. M. S. P. Company's: 1 Rio Dulce: 1 the new Department: 1 the American Line: 1 Arthur V. Briesen: 1 Lee K. Frankel: 1 Eugene A. Philbin: 1 Thomas W. Hynes: 1 Ralph Trautman: 1 Federal trust and interstate-commerce: 1 Klukwan: 1 the Klehini River: 1 Joint High Commission: 1 George Turner: 1 the Right Honourable Lord Alverstone: 1 Louis Amable Jette: 1 the Supreme Court of Quebec: 1 A. B. Aylesworth: 1 Toronto: 1 Tongass Inlet: 1 Wales: 1 Klehini: 1 the Porcupine River: 1 Glacier Creek: 1 the Far Northwest: 1 Message of December 5, 1898: 1 Peckham: 1 J.: 1 the Interparliamentary Union for International Arbitration: 1 this Interparliamentary Union: 1 Oriental Empire: 1 Mukden: 1 the Yalu River: 1 the Auditor for the State: 1 the Louisiana Purchase Exposition: 1 Wagon: 1 W. A. Richards: 1 F. H. Newell: 1 Chief Hydrographer: 1 Fort Riley: 1
Captain Pershing: 1 Moro: 1 the Office of Naval Intelligence: 1 the Board of Inspection: 1 the isthmian canal shall be built: 1 the Isthmus of the Republic of Colombia: 1 new Republic: 1 Cuss: 1 position:"The United States: 1 Nevertheless the Government of Colombia: 1 The Government of Colombia: 1 the Colombian Congress: 1 Secretaries Cass and Seward: 1 The de facto Government of Panama: 1 Ehrman:"The: 1 "The Government of Colombia: 1 Beaupre:"The: 1 the Governments of New Granada: 1 Fish: 1 Bayard: 1 the Hay-Herran: 1 ISTHMIAN CANAL CONVENTION: 1 the Congress of the United States of America: 1 John Hay: 1 Philippe Bunau-Varilla: 1 Perico: 1 Naos: 1 Flamenco: 1 ARTICLE IV: 1 the Government of the Republic of Panama: 1 ARTICLE XII: 1 ARTICLE XIII: 1 ARTICLE XIV: 1 ARTICLE XVI: 1 ARTICLE XVII: 1 ARTICLE XVIII: 1 ARTICLE XIX: 1 ARTICLE XX: 1 ARTICLE XXI: 1 XXII: 1 XV: 1 Lucien N. B. Wyse: 1 Wyse: 1 the Universal Panama Canal Company: 1 ARTICLE XXIII: 1 ARTICLE XXIV: 1 ARTICLE XXV: 1 ARTICLE XXVI: 1 a Power or State: 1 the Territories the Federal: 1 Federal power: 1 The Bureau of Labor: 1 the Labor Bureau: 1 "The Bureau of Corporations: 1 Regulate Commerce: 1 the interstate commerce act: 1 Commonwealths: 1 Commission on Housing and Health Conditions: 1 Park Commission: 1 the Gulf coast: 1 the Great Wall of China: 1 the National Live Stock Association: 1 the National Wool Growers' Association: 1 the American Mining Congress: 1 the National Board of Trade: 1 The Canyon of the: 1 The Pension Bureau: 1 the Indian Office: 1 section 5586: 1 National Gallery of Art: 1 the National Public Health: 1 Marine-Hospital Service: 1 the Signal Corps of the Army: 1 the Code of Alaska: 1 Salmon: 1 The Marine-Hospital Service: 1 Kishenef: 1 American Jew: 1 cruiser: 1 Manassas: 1 Regulars and National Guard: 1 army corps: 1 Turkestan: 1 the Giver of Good: 1 race;and: 1 approachthese: 1 State Government: 1 Federal interstate commerce laws: 1 The Legislature of Colorado: 1 Receiver of Public Moneys: 1 the United States Land Office: 1 State Legislatures: 1 Rosen: 1 The Golden Rule: 1 an American Republic: 1 these Custom Houses: 1 the Collectors of Customs: 1 the Government of Santo Domingo: 1 The Generals: 1 Colonels: 1 the Russo-Japanese: 1 the Medical Department of the navy: 1 Gallinger: 1 the Reclamation act: 1 Reclamation: 1 the National Forest Service: 1 the Merchant Marine Commission: 1 Colonial: 1 Englishman: 1 Slav: 1 Anarchist: 1 Anarchistic: 1 Consular Service: 1 coolie immigration: 1 The Civil Service Commissioners: 1 departments and bureau chiefs: 1 inter-State: 1 the Yosemite Valley: 1 the Yellowstone National Park: 1 Federal jurisdiction: 1 Archipelago: 1 the Province of Cavite: 1 the Province of Batangas: 1 the Province of Samar: 1 the Island of Jolo: 1 Moros: 1 the Moro Province: 1 Batangas: 1 the Federal Court: 1 the Executive Council: 1 the Alaskan Indians: 1 Theodore P. Shonts: 1 Charles E. Magoon: 1 Benjamin M. Harrod: 1 Mordecai T. Endicott: 1 Peter C. Hains: 1 Oswald H. Ernst: 1 the Advisory Board of American: 1 European Engineers: 1 Spooner: 1 War's: 1 James Bronson Reynolds: 1 Charles P. Neill: 1 the Bureau of Animal Industry of the Department of Agriculture: 1 the general Government: 1 Beveridge amendment: 1 Holt: 1 Palmer: 1 The interstate commerce act: 1 Pomeroy: 1 The New York Central: 1 Hudson River Railroad Company: 1 Parker: 1 W. H. Taft: 1 North, South, East: 1 Candler: 1 Bishop Galloway: 1 Jelks: 1 The interstate commerce law: 1 Bourbon: 1 Rebates: 1 the Standard Oil Company: 1 Justice White: 1 the Constitution the National Legislature: 1 the States of the Rocky Mountains: 1 Southern Appalachian: 1 The national bank act: 1 Leyte: 1 Yukon Pacific Exposition: 1 the Loyal Ronins: 1 the Japanese Red Cross: 1 Platt amendment: 1 The Second International Conference of American Republics: 1 the First Conference: 1 the Second Conference: 1 World Congress: 1 Drago: 1 Richard Rush: 1 the Government of Brazil: 1 Palacio Monroe: 1 the Second Peace Conference at The: 1 the Second Hague Conference: 1 the Republics of Salvador: 1 the Third International American Conference: 1 the United States of Mexico: 1 the Republics of Guatemala: 1 The Algeciras Convention: 1 The Bering Sea Tribunal: 1 Sims: 1 The Red Cross Conference: 1 Mahan: 1 the Spanish war the Secretaries of War: 1 a National Board: 1 Our Regular Army: 1 Proportionate: 1 Arecibo: 1 Porto Rico's: 1 the Porto Rican Federation of Labor: 1 the Coffee Growers': 1 Association: 1 the Department of War or the Department of State: 1 The U. S. S. Louisiana: 1 Cristobal 1.05 inches: 1 the Ancon Hospital: 1 the Hotel Tivoli: 1 the British Government on the Isthmus of Panama: 1 Gey: 1 Sosa: 1 Cristobal: 1 Front: 1 Rixey: 1 Engineer Stevens: 1 Waring: 1 Surgeon-General Rixey: 1 the Norfolk Navy-Yard: 1 Commission's: 1 Mount Hope: 1 Colon and Cristobal: 1 Grant's Administration: 1 Titanic: 1 Shanton: 1 O'Connor: 1 St. Lucia: 1 ample house: 1 the Secret-Service: 1 seaman's: 1 Panamans: 1 French Company: 1 The Young Men's Christian Association: 1 the Chief Engineer: 1 ridge: 1 Culebra: 1 Thomases: 1 the Great Northern Railroad: 1 St. Louis—the: 1 General Nettleton: 1 Augustus P. Blocksom: 1 Leonard A. Lovering: 1 Ernest A. Garlington: 1 the Inspector-General of: 1 Southerner: 1 Blocksore: 1 A. B. Nettleton: 1 ex-Union: 1 Fort Brown: 1 Penrose: 1 Appendix: 1 James Wilkinson: 1 Sixtieth: 1 The Eleventh New York: 1 the Fifth Missouri Cavalry: 1 the Fourth Missouri Cavalry: 1 the Thirteenth Army Corps: 1 the Twentieth Illinois: 1 Confederates: 1 Twiggs: 1 the Army of Northern Virginia: 1 the Charlotte Observer: 1 Speer: 1 this the National Government: 1 the National Interstate Commerce Commission: 1 Inland Waterways Commission: 1 the States of the Great Plains: 1 Rockies: 1 Sierras: 1 White Mountain: 1 George W. Goethals: 1 William L. Sibert: 1 Civil Engineer H. H. Rousseau: 1 U. S. Navy: 1 J. C. S. Blackburn: 1 U. S. Army: 1 Alfred Noble: 1 Frederic P. Stearns: 1 John R. Freeman: 1 Alaskan-Yukon-Pacific Exposition: 1 The State of: 1 Hermitage: 1 Hermitage Association: 1 the Vicksburg National Park: 1 the Congress for the Thirteenth Census: 1 Census Bureau: 1 Republics of South America: 1 trans-Atlantic: 1 Baal: 1 The Medical Corps: 1 Revenue-Cutter Service: 1 Rear-Admiral: 1 the Medical Corps of the Navy: 1 The Academy: 1 Richard Wainwright: 1 Robert S. Griffin: 1 Albert L. Key: 1 Charles J. Badger: 1 The Second International Peace Conference: 1 The Hague on the 15th of June: 1 follows;"It: 1 the Third American Conference at Rio: 1 Chapter III: 1 the Dogger Bank: 1 Second Conferences: 1 the Queen of the Antilles: 1 the French Exposition: 1 the Departments of the Treasury: 1 Empire of China: 1 the Presidents of Mexico: 1 the Pan American Conference: 1 the International Bureau of American Republics: 1 Commerce and Labor: 1 the Anthracite Strike Commission: 1 Waterways Commission: 1 the Currency Commission: 1 the Constitution the United States: 1 The Central Government: 1 the National Government of combinations: 1 the National Government of the great interstate corporations: 1 Bulletin: 1 COURTS: 1 the House Committee on the Judiciary: 1 associations: 1 Adriondacks: 1 North China: 1 Frank N. Meyer: 1 the Bureau of Plant Industry: 1 Department of Agriculture: 1 Marco Polo: 1 Shansi and Shensi: 1 Polo: 1 the Hun Ho: 1 barbaric: 1 Mongol: 1 the Sungari River: 1 Jehol: 1 the Mongolian Desert: 1 the scientific bureaus of the Government: 1 Stegomyia: 1 INDIAN: 1 The National Bureau of Education: 1 International Congress on Tuberculosis: 1 the Department of Commerce and Labor the Government Printing Office: 1 the Joint Committee: 1 INDEPENDENT BUREAUS: 1 Bureau of Fisheries: 1 the Bureau of Fisheries: 1 LATIN-AMERICAN: 1 The International Bureau of the American Republics: 1 Australiasia: 1 the Panamal Canal: 1 the Territory of Hawaii: 1 coolie labor: 1 Hitherto this: 1 CUBA: 1 NATIONAL GUARD: 1 The North Dakota: 1 The Board of Visitors: 1 the Bureauof Corporations: 1 the InterstateCommerce Commission: 1 theDingley Act: 1 Dingley Act: 1 theMississippi: 1 Asiaof: 1 Meantimewe: 1 Executivein: 1 Republicanplatform: 1 investmentas: 1 the Departments of Agriculture andof Commerce: 1 The Panama Canal: 1 onthe east coast: 1 Executiveupon: 1 theIsthmus: 1 the Isthmus of a board of competent: 1 Gatundam: 1 Goethalsand: 1 Philippinesare: 1 PortoRico: 1 Northerners: 1 theFederal Government: 1 Southern Statesof: 1 theSouth: 1 Finance Committee: 1 Loan: 1 Spreckels Sugar Refining Company against: 1 McClain: 1 The Ways and Means Committee of the House: 1 Payne Tariff: 1 Schedule B--: 1 F: 1 Dingley lawby Payne: 1 of---------------------- 78 3: 1 Schedule N: 1 Schedule A: 1 Schedule H: 1 rates)G: 1 Schedule A. Chemicals: 1 Wines: 1 Pinchot: 1 Aldrich: 1 the North Atlantic Coast: 1 the Arbitral Tribunal: 1 the Canadian International boundary: 1 the Passamaquoddy Bay: 1 The International Fisheries Commission: 1 the Convention of April 11, 1908: 1 the St. John River: 1 The Second Peace Conference: 1 Article 7 of the Convention: 1 the Declaration of London: 1 the International Conference on Maritime Law: 1 International Conference for the: 1 Continental: 1 Belgian Agents: 1 The Liberian Government: 1 The Norwegian Government: 1 The European Powers: 1 Majesty Mehmed V: 1 Sultan of Turkey: 1 the Governments of Bolivia: 1 the Governments of Peru: 1 International Agricultural Exhibition: 1 Departments of this Government: 1 Exhibition: 1 Panaman: 1 the Emery Company: 1 Alsop and Company: 1 Edward: 1 the Far East this Government: 1 The Imperial Chinese Government: 1 the Opium Conference: 1 the Chinese-Japanese Conventions: 1 South Manchurian: 1 Antung-Mukden Railroads: 1 THE DEPARTMENT OF STATE: 1 Divisions of Latin-American: 1 Far Eastern Affairs: 1 the Department of a: 1 the Executive Order: 1 the Act of August 5, 1909: 1 the Bureau of Trade Relations of the Department of State: 1 Section 2: 1 the Bureau of Trade Relations: 1 a Committee on Public Expenditures: 1 Maximum and Minimum: 1 THE NEW TARIFF BOARD: 1 Promoted: 1 Chesapeake Bay: 1 the Department of a military: 1 Naval Observatory: 1 EXPEDITION: 1 State Bar Associations: 1 the following:"The Republican party: 1 Sherman Anti-trust Law: 1 the "Interstate Commerce Law: 1 Capitol Dome: 1 ARIZONA: 1 States of: 1 LIGHT-HOUSE BOARD: 1 The Light-House Board: 1 THE WHITE SLAVE TRADE: 1 the Bureau of Immigration: 1 The White Slave Trade: 1 the Medical Department of the Army: 1 the Medical Department of the Navy: 1 the "Bureau of Public Health: 1 the Census Act: 1 CongressFREEDMAN'S SAVINGS AND TRUST COMPANY: 1 the "United States Court of Commerce: 1 the "Elkins Act: 1 section 20: 1 section 23: 1 Reasons: 1 the Court of Customs Appeals: 1 the Commerce Court: 1 The Commerce Court: 1 section 15: 1 the Interstate Commerce Committee: 1 Sherman Anti-Trust Act: 1 the" Sugar Trust: 1 the Sugar Trust: 1 Sugar Trust: 1 The Homestead Act: 1 the Lower House: 1 The Mississippi River: 1 the Permanent Court of The Hague: 1 Fisheries: 1 the Fisheries Convention of 1818: 1 the Orinoco Steamship Company: 1 the Prize Court Convention: 1 The Identic Circular Note: 1 Arbitral Court of Justice: 1 GREAT BRITAIN: 1 CANADA: 1 the International Fisheries Commission: 1 PORTUGAL: 1 the Treaty of 1862: 1 the American Naval Officers: 1 the American Commission: 1 TURKEY: 1 the Special Embassy: 1 Ottomans: 1 MONTENEGRO: 1
National Skoupchtina: 1 King of Montenegro: 1 Hankow: 1 the Yangtse Valley: 1 Treaty of 1903: 1 the International Exchange Commission: 1 Their Majesties: 1 the Russo-Japanese Agreement: 1 Their Imperial Highnesses Princes: 1 Tsai-tao: 1 Tsai-Hsun of China: 1 Prince Higashi Fushimi: 1 Prince Tokugawa: 1 the House of Peers of Japan: 1 the Fourth Pan-American Conference: 1 the Delegation of the United States: 1 the International Conference of American States at Buenos Aires: 1 Pan American Union: 1 American Governments: 1 The Hague Convention for the pacific settlement: 1 the United States-Mexican Boundary Commission: 1 Tex: 1 The Republic of Honduras: 1 the Honduranean Government: 1 the Madriz Government: 1 Argentine Republic: 1 AMERICAN BRANCH BANKS: 1 the Courts of the: 1 DIPLOMATIC: 1 Section 1753: 1 the Executive Orders: 1 EMBASSY: 1 the Government of suitable residences: 1 the House Committee on Foreign Affairs: 1 CUT: 1 the Auditor: 1 the Auditor for the Post Office Department: 1 Final Estimates: 1 Original Estimates: 1 Total Estimates: 1 Legislature$13,426,805.73$13,169,679.70$13,169,679.70$12,938,048.00: 1 + 420,109.14Public: 1 revenues---------------10,634,122.6310,634,122.6310,634,122.63-10,634,122.65-10,634,122.63: 1 Agriculture19,681,066.0017,681,136.0017,753,931.2417,821,836.00+ 1,927,134.76+ 1,859,230.00-: 1 the Port of New York: 1 Port: 1 Collector: 1 the Naval Officer: 1 The Naval Officers: 1 York, Me: 1 Md.: 1 the Federal Courts: 1 Printing: 1 the Division of Public Moneys: 1 the Division of Loans and Currency: 1 Congress the Government's: 1 THE PAYNE TARIFF ACT: 1 TARIFF BOARD: 1 the nonpartisan Monetary Commission of: 1 the Volunteer Act: 1 the Philippine War: 1 Volunteer Act: 1 this Volunteer Bill: 1 The Army Engineers: 1 coast: 1 Corregidor Island: 1 Pacific Naval: 1 To Provide a Civil Government: 1 Purposes: 1 CANAL: 1 the Tehuantepec Railroad: 1 Suez Canal: 1 MAINTENANCE OF CANAL: 1 Port Said: 1 the interstate commerce law a provision prohibiting interstate commerce: 1 NATIONAL INCORPORATION: 1 the Lord Chancellors of England: 1 RELIEF OF SUPREME COURT: 1 UNNECESSARY APPEALS: 1 United States Circuit Courts of Appeals: 1 the Circuit Courts of Appeals: 1 Circuit Courts of Appeals: 1 the Circuit Court of Appeals of the third circuit: 1 JUDICIAL: 1 the Federal Judges: 1 Circuit Judges: 1 the Post-Office Department of this Government: 1 Postmasters: 1 FRANKING: 1 Departmental: 1 the Postal Savings Banks: 1 Aides: 1 the Commission on Naval Reorganization: 1 Justice Moody: 1 the naval department: 1 Court of Inquiry: 1 the Major-General Commandant: 1 Inspector: 1 The Naval Supply: 1 the Naval Supply Account: 1 JOHN PAUL JONES: 1 Robert Edwin Peary: 1 APPEALS: 1 the Committee on Public Lands: 1 ALASKAN RAILWAYS: 1 Pacific coast: 1 THE CENSUS BUREAU: 1 the Light-House Board: 1 the Pribiloff Islands: 1 Pribiloff: 1 The Coast and Geodetic Survey: 1 the Erdman Act: 1 PHOSPHOROUS MATCHES: 1 SUSPENSION: 1 EMERGENCIES: 1 the Government of extra compensation: 1 WORKMEN: 1 the International Congress on Industrial Insurance: 1 DEPARTMENTS: 1 a Bureau of Health: 1 The Southern Pacific Railroad Company: 1 the Southern Pacific Railroad Company: 1 The Commissioners of the District: 1 the Botanic Garden: 1 Washington Barracks: 1 the Meridian Hill: 1 Montrose: 1 the Klingle Ford: 1 the State of the land: 1 Arlington Cemetery: 1 Rosslyn: 1 Virginia Senators and Representatives: 1 the Board of Education: 1 the School Board: 1 the Superintendent of Education: 1 PERMANENT IMPROVEMENTS: 1 Health Departments: 1 Judson: 1 FREEDMEN'S BANK: 1 the Freedmen's Bank: 1 Arthur T. Hadley: 1 Frederick Strauss: 1 Frederick N. Judson: 1 Walter L. Fisher: 1 B. H. Meyer: 1 William E. S. Griswold: 1 Reciprocity: 1 the American Tobacco Trust: 1 the Standard Oil and Tobacco: 1 EQUITY: 1 the Supreme and Circuit Courts: 1 Tobacco: 1 The American Tobacco Company: 1 Liggett &: 1 Meyers Tobacco Company: 1 the P. Lorillard Company: 1 the R. J. Reynolds Tobacco Company: 1 United Cigar Stores: 1 the Liggett &: 1 the Lorillard Company 22.82 per cent: 1 the Reynolds Company 2.66: 1 American Tobacco Company: 1 the Tobacco Trust: 1 Circuit: 1 Noyes: 1 SUPPLEMENTAL: 1 NEEDED: 1 AMENDMENT: 1 AID COURTS IN TRUST DISSOLUTIONS: 1 Tobacco Trust: 1 the Comptroller of the Currency or: 1 IMPORTANCE OF THE ANTI-TRUST ACT: 1 OF ALSOP &: 1 CO.: 1 Alsop &: 1 Co.: 1 MEXICO: 1 Aleshire: 1 -Mexicans: 1 Agua Prieta: 1 Sloan: 1 Occurrence: 1 Tia Juana: 1 Senor de la Barra: 1 Senor Francisco 1: 1 Bernardo Reyes: 1 the Diaz Government: 1 CENTRAL AMERICA-HONDURAS: 1 these Central American Republics: 1 The Government of Nicaragua: 1 THE PAN AMERICAN UNION: 1 CHINESE: 1 the Provinces of Hunan and: 1 Hupeh: 1 NEUTRAL FINANCIAL ADVISER: 1 the Dutch Java Bank: 1 Chinese Empire: 1 Haichi: 1 Admiral Ching: 1 Count Togo: 1 Moroccan: 1 The Ottoman Empire: 1 GEORGE V. : 1 George V.: 1 Article 11: 1 Albert Jaegers: 1 von Steuben: 1 RUSSIA: 1 The National Constituent Assembly: 1 CONFERENCES: 1 article 7 of The Hague: 1 NECESSARY: 1 the International Union for the Protection of Industrial Property: 1 the Paris convention of March 20, 1883: 1 INTERNATIONAL OPIUM COMMISSION: 1 the International Opium Commission: 1 BUENOS AIRES CONVENTIONS: 1 the Fourth Pan American Conference at Buenos Aires: 1 FOREIGN TRADE RELATIONS: 1 TYTE UNITED STATES: 1 the National Red Cross: 1 the Diplomatic and Consular Service: 1 Lowden: 1 the Sixty-first: 1 the Tariff Board on Schedule K.: 1 i. e.: 1 unscoured: 1 The Tariff Board: 1 SUM: 1 the Panama Canal 3 per cent: 1 THE TREASURY DEPARTMENT: 1 Monetary Commission: 1 THE WAR DEPARTMENT: 1 the Adjutant General's: 1 the Inspector General's: 1 Subsistence: 1 General Staff Corps: 1 i5,ooo: 1 ARLINGTON: 1 THE PANAMA CANAL: 1 Spooner Act: 1 REPAIRS: 1 PHILIPPINE: 1 Dickinson: 1 the Sanitary District of Chicago: 1 THE DEPARTMENT OF JUSTICE: 1 LIABILITY: 1 TO PREVENT: 1 chapter II: 1 Code: 1 POST OFFICE: 1 POSTAL-SAVINGS SYSTEM: 1 the City Delivery Service: 1 THE NAVY DEPARTMENT: 1 North Chicago: 1 unnecessary navy: 1 COUNCIL OF NATIONAL DEFENSE: 1 the council of national defense: 1 COMMISSION: 1 the Commission on Economy and Efficiency: 1 THE STATE DEPARTMENT: 1 Divisions of LatinAmerican Affairs: 1 Western European Affairs: 1 Division of Information: 1 CONSULAR: 1 DIPLOMATIC CORPS Expert: 1 Department of State: 1 the Consular Service: 1 STATISTICS AS TO: 1 the Diplomatic and Consular Services: 1 student interpreter corps: 1 SUCCESSFUL EFFORTS: 1 the Governments of Chile: 1 TacnaArica: 1 The Republics of Central America: 1 ENFORCEMENT OF NEUTRALITY LAWS: 1 foci: 1 SUPPLEMENTARY: 1 FUR SEAL TREATY: 1 FINAL SETTLEMENT OF NORTH ATLANTIC: 1 the North Atlantic Coast Fisheries Arbitration Tribunal: 1 Article I of the treaty of October 20, 1818,: 1 Govern: 1 INTERNATIONAL COMMISSION OF JURISTS: 1 the International Commission of jurists: 1 OPIUM CONFERENCE-UNFORTUNATE: 1 the Opium Conference at The Hague: 1 Cyrenaica: 1 Tripolitania: 1 the U. S. S. Scorpion: 1 the Liberian Government: 1 MOROCCO: 1 JAPAN: 1 the American Commonwealth: 1 Mutsuhito: 1 Knox: 1 the Coast Artillery School: 1 Fort Monroe: 1 Britannic Majesty King: 1 the United States Coast Artillery: 1 the Chilean Coast Artillery School: 1 Guayaquil & Quito Railway Co.: 1 the Government of Ecuador: 1 John Celestine Landreau: 1 the American Public Health Service: 1 the Third International Students Congress: 1 The Republic of Uruguay: 1 Caceres: 1 The Republic of Cuba: 1 the Province of Oriente: 1 The Cuban Government: 1 the Government of Cuba: 1 Fiscal: 1 Military and Insular Affairs: 1 the Jackson Administration: 1 the National Reserve Association: 1 section 38: 1 the President's Commission on Economy and Efficiency: 1 THE HOME ARMY: 1 THE NATIONAL GUARD: 1 The Organized Militia: 1 NATIONAL VOLUNTEERS: 1 THE SUPPLY CORPS: 1 the Service Corps: 1 the supply corps: 1 THE PANAMA CANAL During: 1 Emory R. Johnson: 1 PANAMA CANAL TREATY: 1 COL: 1 the Bathtub Trust: 1 WORKMAN'S: 1 Concerning the Work of the Departments of the Post Office: 1 Cabinet -at: 1 the Cabinet on the floor of: 1 Ilouse: 1 Commentaries: 1 GEO: 1 W. B. ALLISON: 1 D. W. VOORHEES: 1 J. G. BLAINE: 1 M. C. BUTLER: 1 JOHN J. INGALLS: 1 J. T. FARLEY.It: 1 POSTAL SAVINGS BANK SYSTEM: 1 The Postal Savings Bank System: 1 B. Group A: 1 B.: 1 The Postmaster General's: 1 expIoitation: 1 Land Department: 1 the Remsen Board: 1 the Bureau of Chemistry: 1 PANAMA-PACIFIC INTERNATIONAL EXPOSITION: 1 Potomac Park: 1 Rock Creek: 1 the Lincoln Monument: 1 whenthe House of Representatives: 1 Nationnow: 1 areor: 1 Men's: 1 day—very: 1 it—now: 1 the Battle of Gettysburg: 1 quartermaster's: 1 Day: 1 Julys: 1 Dominion of Virginia: 1 Hercules: 1 The Agricultural Department of the Government: 1 Our Bureau of Mines: 1 Governments of similar: 1 Huertistas: 1 no House of Lords: 1 this Declaration of Independence: 1 the Declaration of Independence on this: 1 the Sixty-third: 1 the Parliament of Great Britain: 1 the National Guard of the States: 1 Restrictions: 1 the Virginia Bill of Rights: 1 Pan-Americanism: 1 the Quartermaster Corps: 1 the Naval academy: 1 the aviation corps: 1 the General Board of the Department: 1 Ladies of the Association: 1 Newtonian: 1 the Congress of the consideration: 1 the Interstate Commerce Commission's: 1 the Corrupt Practices Act: 1 the Sixty-fourth: 1 The Entente Powers: 1 the organized major force: 1 the Entente-Allies': 1 the Entente-Allies: 1 the Houses of Congress: 1 the Lyman M. Law: 1 God's Providence: 1 Cunningly: 1 Intrigue: 1 Count Tarnowski: 1 Royal: 1 Section 4067: 1 Sections 4068, 4069: 1 Sections 4069: 1 the United States Marshal: 1 supreme crisis: 1 Section 5: 1 the Board of Commissioners of the District of Columbia: 1 the Territories of Alaska: 1 the City Clerk: 1 UNWILLING: 1 mass: 1 the AustroHungarian Empire: 1 the Congress of Vienna: 1 section 4067: 1 Central Powers: 1 Teutonic: 1 Lloyd George: 1 I. 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FOREIGN AFFAIRSAmerican: 1 the Nationalist Government of China: 1 the European Defense Community: 1 Joint Commission on Foreign Economic Policy: 1 the Budget Message: 1 the National Security Training Commission: 1 the Office of Defense Mobilization: 1 the Committee on Ways and Means of the House of Representatives: 1 the Renegotiation Act: 1 NATIONAL HIGHWAYSTo: 1 ½¢: 1 PROBLEMSAlong: 1 The Labor Management Relations Act of: 1 Committees: 1 Hospital Survey and Construction Act: 1 EDUCATIONYouth: 1 the Federal Government;Redirection: 1 Continuation of the public housing program: 1 VETERANS ADMINISTRATIONThe internal reorganization: 1 NATO.In Asia: 1 Manila Pact: 1 The Pacific Charter: 1 Internal Security Division: 1 the Immigration and Naturalization Service: 1 Actions: 1 My Budget Message: 1 the Columbia River Basin: 1 Priest Rapids: 1 Coosa River: 1 Markham Ferry: 1 Colorado River Basin: 1 the Conference of State Governors: 1 National Highway Program: 1 The Council of Economic Advisers: 1 an Office of Coordinator of Public Works: 1 the Agricultural Act: 1 the Small Business Act: 1 the White House Conference: 1 Eight Hour Laws: 1 the Postal Field Service: 1 the Group Life Insurance Act: 1 the Committee on Retirement Policy for Federal: 1 a Federal Advisory Commission: 1 Arts: 1 the Reorganization Act: 1 the Commission on Organization of the Executive Branch: 1 Highway Legislation Legislation: 1 Federal and Federally-assisted construction: 1 the House of Representatives]To: 1 USSR: 1 AgricultureOur: 1 RedevelopmentWe: 1 Highway Legislation: 1 Title 1: 1 Martin, Delegates and Alternates: 1 New Year's Day: 1 Henrik Ibsen: 1 Youth for Eisenhower: 1 the American Heritage: 1 the Soil Bank: 1 The Party of the Future: 1 The Party: 1 Our Party: 1 Conscience Whigs: 1 transcendentalists: 1 Atoms for Peace Plan: 1 Curtain: 1 the National Archives Building: 1 Prologue: 1 the Republican State Convention: 1 Bolsheviks: 1 the Eastern Hemisphere: 1 The Suez Canal: 1 Eurasia: 1 Mecca: 1 the Extraordinary Fifth Session: 1 the Supreme Soviet: 1 the Sovietization of the Baltic: 1 anti-Soviet: 1 the Warsaw Treaty: 1 the Tripartite Declaration of May 25, 1950: 1 our Declaration of November 29, 1956: 1 Mid East: 1 the Mutual Security Act: 1 and(4: 1 dynamos: 1 RESEARCH: 1 EDUCATION: 1 RESEARCHSixth: 1 Science: 1 CONCLUSIONThese: 1 Outlays of the Department of Agriculture: 1 the Union Archives Video & Text of Presidential State of the Union Addresses Executive & Cabinet Links Links to Executive: 1 Federal & Cabinet: 1 Menus & Entertainment: 1 White House State & Official Dinners: 1 Textual Resources: 1 Info: 1 the Federal Election Commission: 1 IV.America: 1 citizens--: 1 Butler: 1 Symington: 1 The Rights of Man,”—the civil and economic rights essential: 1 Platform: 1 Stuart Symington: 1 Paul Butler: 1 Henry II: 1 Richard Cromwell: 1 Coolidge following: 1 a New Frontier: 1 Woodrow Wilson’s: 1 Scriptural: 1 David Lloyd George: 1 Meza: 1 Reck: 1 the National Council of Churches: 1 Quaker: 1 Article VI of the Constitution: 1 the Virginia Statute of Religious Freedom: 1 McCafferty: 1 Carey: 1 De Gaulle: 1 non-Catholics: 1 HOWARD K. SMITH: 1 Jimmy Hoffa: 1 the Northwest United States: 1 G.N.P.: 1 Charles Warren: 1 Stuart Novins: 1 Bob Fleming: 1 the President's Committee on Price Stability and Economic Growth: 1 F.B.I.: 1 Howard K. Smith: 1 FRANK McGEE: 1 Paul Niven: 1 Alvin Spivak: 1 Harold R. Levy: 1 The Strategy for Peace: 1 Arthur Gardner: 1 Earl Smith: 1 Raul Castro: 1 Southern Russia: 1 MR: 1 the Declaration of Independence and in the Constitution: 1 the Democratic Congress -: 1 the Democratic Congresses: 1 Monroney: 1 Rockefeller Brothers: 1 the Gaither Report: 1 O.A.S.: 1 the Inter-American Bank: 1 Wilkes-Barre: 1 Eisenhower-Nixon Administration: 1 the Senate Agricultural Committee: 1 Callins: 1 Spruance: 1 the Press Club: 1 k- Korean War: 1 BILL SHADEL: 1 Charles Van Fremd: 1 Douglass Cater: 1 Roscoe Drummond: 1 New York Herald Tribune: 1 people - Republicans: 1 Sudetenland: 1 Danzig: 1 a new Administration: 1 Chiang Kai-shek - and this Administration: 1 uh - Robertson: 1 Twining: 1 the re- Republic of China: 1 Thruston Morton: 1 the Tachen Islands: 1 Dave Garroway: 1 The Ku Klux Klan: 1 Michael DiSalle: 1 the Steelworkers Union: 1 the next Administration: 1 Republican Ambassadors: 1 the American Conference - Inter-American Conference: 1 a Bureau of African Affairs: 1 Cubas: 1 the Union of South Africa: 1 U.S.I.A.: 1 anti-Batista: 1 Arbenz: 1 the Bogota Conference - this: 1 your Information Service: 1 the Atomic Energy Committee: 1 Walter Cronkite's: 1 the Hungarian revolution: 1 the Eastern Europe: 1 Gomulka: 1 John Chancellor's: 1 Chiang Kai-shek's: 1 Pericles: 1 Athenians: 1 Arabella: 1 dedicationthese: 1 the Bay Colony: 1 the Bay State: 1 Beacon Hill: 1 Akin: 1 Nehru: 1 the U.S. Employment Offices: 1 Efficient: 1 Flag: 1 Housing and Urban Affairs Department: 1 Freedom and Communism: 1 Inter-Departmental Task Force: 1 National Peace Corps: 1 the Mutual Defense Assistance Control Act: 1 servants--: 1 the National Government-: 1 the U.S. Government: 1 the Ambassadorial Corps: 1 Benito Juarez: 1 Operation Pan America: 1 Alliance for Progress--Alianza para Progreso: 1 the Inter-American Economic and Social Council: 1 IA-ECOSOC: 1 the Economic Commission: 1 the Inter-American Development Bank: 1 Inter-American: 1 San Martin: 1 Catledge: 1 Revolutionary Council: 1 Cardona: 1 the Revolutionary Council: 1 the New York Herald Tribune: 1 Horace Greeley: 1 Greeley: 1 Charles Dana: 1 Engels: 1 Tribune: 1 Leninism, Stalinism: 1 Office of War Information: 1 Athenian: 1 Solon: 1 the Seventeenth Century: 1 Francis Bacon: 1 the Joint Economic Committee: 1 Inevitably: 1 Employment Service: 1 .the United States: 1 American Citizens Abroad: 1 Estate Tax on Property Located Abroad: 1 Dividends: 1 the Dividend Credit and Exclusion: 1 the 1954 Act: 1 the Federal treasury: 1 Tighter: 1 Capital Gains: 1 Depreciable Business Property: 1 The Internal Revenue Service: 1 the Revenue Service: 1 Inventory Reporting: 1 the Fellows of this University: 1 Lowell: 1 the Canadian Houses of Parliament: 1 Je: 1 the Maritime Provinces: 1 Henry Thoreau: 1 Westward: 1 War Memorial: 1 inter-American Community: 1 the Development Assistance Group: 1 a Development Center: 1 the UN Emergency Force: 1 South East Asia: 1 the Ten Nations Commission on: 1 Disarmament-Canada: 1 Edmund Burke: 1 Advisory Committee on Labor-Management Policy: 1 a Presidential Contingency Fund: 1 Red Chinese: 1 the Military Assistance Program: 1 INTELLIGENCE: 1 The Office of Civil and Defense Mobilization: 1 the Office of Emergency Planning: 1 enlarged Disarmament Agency: 1 the National Space Council: 1 Rover: 1 the Space Committees: 1 the Appropriating Committees: 1 X. CONCLUSION: 1 Captain of the Western World: 1 the Federal Civil Service: 1 the Graduating Class: 1 Captain Mahan: 1 The Atlantic Community: 1 Troika: 1 Damocles: 1 United Nations Peace Force: 1 the Disarmament Plan: 1 Disarmament Program: 1 --Fifth: 1 the United Nations Emergency Forces: 1 the United Nations Decade of Development: 1 Pushkin: 1 Boris Godunov: 1 The Western Allies: 1 The Western Powers: 1 Sanford: 1 the Massachusetts Legislature: 1 Boylston Professor of Rhetoric and Oratory: 1 the Virginia House of Burgesses: 1 Bizerte: 1 The American Eagle: 1 Gavan: 1 this Veterans Day: 1 rocky hill: 1 Armistice Day: 1 Augustine: 1 The First World War: 1 Fredericksburg: 1 Cemetery: 1 race--: 1 turneth: 1 San Juan Hill: 1 the University of Washington: 1 NAM: 1 Robert Roosa: 1 the Federal Reserve Bank: 1 Robert McNamara: 1 John McCone: 1 Point 4: 1 The Communist Bloc: 1 The South Koreans: 1 The Reciprocal Trade Agreements Act: 1 the NAM Economic Advisory Committee: 1 The United Kingdom: 1 Goldberg--Goldberg: 1 this Pearl Harbor: 1 the trade union movement: 1 a Presidential Advisory Committee on: 1 Labor-Management Policy: 1 the Congo-Adoula: 1 The Gross National Product: 1 Conant: 1 CIO: 1 Sam" Rayburn: 1 the Manpower Training and Development Act: 1 the Youth Employment Opportunities Act: 1 Federal Pay Reform: 1 the Internal Revenue: 1 the Bureau of Narcotics: 1 Department of Urban Affairs: 1 National Institutes of Health: 1 the Food and Drug: 1 House Committee: 1 Civilization: 1 H. G. Wells: 1 Guard Divisions: 1 Hammarskiold: 1 a united Congo: 1 Charles Lindbergh's: 1 International Communications Satellite: 1 Food for Peace, Export-Import Bank: 1 Agency for International Development: 1 design"-it: 1 Bolshevik: 1 The Reciprocal Trade Act: 1 Trade Expansion Action: 1 the National Security Amendment: 1 non-European: 1 the Board of Regents: 1 Coast: 1 Berkeley: 1 This University of California: 1 Oliver Wendell Holmes: 1 French Marshal Lyautey: 1 Lemnitzer: 1 General Decker: 1 the Superintendent of West Point: 1 the Air Academy: 1 Tucker: 1 the Indo-Chinese Peninsula: 1 Ernie Pyle: 1 Roger Blough: 1 Henry Ford: 1 John Hay Whitney: 1 Henry Luce: 1 Chester Bowles: 1 Dean Acheson: 1 William Howard Taft: 1 Federal fiscal: 1 Federal deficits: 1 Proxmire: 1 the Bank for International Settlement: 1 Basel: 1 Stop the World: 1 Pitzer: 1 Wiley: 1 this State of: 1 William Bradford: 1 the Plymouth Bay Colony: 1 Mariner: 1 Tiros: 1 George Mallory: 1 Mount Everest: 1 Meredith versus Fair: 1 James Meredith: 1 Tuttle: 1 Hutcheson of: 1 Rives of: 1 Wisdom of Louisiana: 1 Gewin: 1 the Mississippi National Guard: 1 Southland: 1 the State of Lucius Lamar: 1 the 155th Infantry: 1 Jake Lindsey: 1 the Panama Canal, Cape Canaveral: 1 Hudson Bay: 1 the Rio Pact of 1947: 1 the 87th: 1 the Organ of Consultation: 1 the Rio Treaty: 1 The United Nations Charter: 1 pre-Korean: 1 Succeed: 1 the American Peace Corps: 1 European Allies: 1 Rudolf Anderson: 1 James Allen Johnson: 1 Sergeant Gerald Pendell: 1 the "Good Ship Union: 1 Kefauver: 1 Fulton: 1 Evins: 1 Bass: 1 Tom Murray: 1 John Adams': 1 the Government Service: 1 Bishop McTyeire: 1 Patience: 1 Zuckert: 1 Fraser: 1 Dear Sir: "Would you desire: 1 the Air Force Cadet Wing: 1 the Cadet Wing: 1 Cadet Marvin B. Hopkins: 1 Mach 2: 1 Norstad: 1 Eglin: 1 the Island of Okinawa: 1 Bob Byrd: 1 the American University: 1 the Methodist Church: 1 John Fletcher Hurst: 1 Bishop Hurst's: 1 Methodists: 1 John Masefield: 1 the Northern District: 1 Justice Harlan: 1 Lass: 1 The Soviet Government: 1 the United Nations Secretariat: 1 United Nations Decade of Development: 1 a Peace Corps: 1 United Nations Conference on Trade: 1 the United Nations' Decade of Development: 1 Buddhist: 1 Protestant: 1 Cardinal: 1 the Kingdom of God: 1 the International Law Commission: 1 Charter: 1 Archimedes: 1 McKay: 1 Ted Moss: 1 the States of the West: 1 Theodore: 1 the Mormons of a century ago: 1 Winter Quarters: 1
the Great Salt Lake: 1 Custer: 1 Plimpton: 1 our Disarmament Advisory Committee: 1 Jim Reed: 1 Ramey: 1 Dick Reuter: 1 the Great Republic's: 1 Robert Frost's: 1 the Second War: 1 I. Like: 1 us harshly: 1 Indian Reservations: 1 Hill-Burton Act: 1 Indonesian: 1 East African: 1 The Defense Department: 1 Defense Department: 1 the Pomona Division of General Dynamics: 1 Pomona: 1 Shillelagh: 1 the Naval Ordnance Test Station: 1 China Lake: 1 the Joseph P. Kennedy: 1 the Chicago School Board: 1 Atomic Committee: 1 Lincoln Day: 1 Administrator Weaver: 1 General de Gaulle's: 1 Pearson: 1 Walter Jenkins: 1 Adolfo Lopez Mateos: 1 Mateos: 1 Kuchel: 1 Iakovos: 1 John Plumides: 1 John Pappas: 1 John Brademas: 1 Mike Manatos: 1 George Vournas: 1 Walter Tobriner: 1 the District of Columbia Board of Commissioners: 1 Tobriner: 1 Laurence K. Walrath: 1 Walrath: 1 James L. Robertson: 1 Hugh Owens: 1 the Oklahoma Securities Commission: 1 Frank Stanton: 1 the Columbia Broadcasting System: 1 the U.S. Advisory Commission on Information: 1 The Department of Labor's: 1 Consumers Price: 1 the National Association of Purchasing Agents: 1 GS-18: 1 the Commission on Heart Disease, Cancer: 1 Stroke: 1 Michael E. DeBakey: 1 Baylor University College of Medicine: 1 Barry Bingham: 1 Marion Folsom: 1 Emerson Foote: 1 Howard Rusk: 1 Paul Sanger: 1 Edward Dempsey: 1 Hugh Hussey: 1 Irving S. Wright: 1 J. Willis Hurst: 1 Charles W. Mayo: 1 Sidney Farber: 1 R. Lee Clark: 1 E. M. Papper: 1 Philip Handler: 1 Florence Mahoney: 1 Samuel Bellet: 1 John Meyer: 1 Marion Fay: 1 Helen Taussig: 1 Jane Wright: 1 John Carter: 1 Frank Horsfall: 1 Alfred Gruenther: 1 Arthur Hanisch: 1 James F. Oates: 1 David Sarnoff: 1 Henry Du Pont: 1 James Fosburgh: 1 William Benton: 1 Marshall Field: 1 the Council of the Organization of American States: 1 Inter-American Committee on the Alliance for Progress The Committee's: 1 Carlos Sanz de Santamaria: 1 Q. Mr: 1 the U.S. Public Health Service: 1 the Surgeon General: 1 Atomic Energy: 1 Apollo-Gemini: 1 Defense McNamara: 1 the American Territories: 1 Milton Eisenhower: 1 Leonard Bernstein: 1 Katherine Anne Porter: 1 Albert W. Dent: 1 Dillard University of New Orleans: 1 Michael P. Walsh: 1 Boston College: 1 William Hagerty: 1 Drexel Institute of Technology: 1 Melvin W. Barnes: 1 the House Committee on Merchant Marine: 1 Roger Stevens: 1 a Presidential Board on the Arts: 1 Krag of: 1 Denmark Day: 1 the New York World's: 1 Willy Brandt: 1 Ludwig Erhard: 1 the foreign Relations Committee of the Senate: 1 the LBJ Company: 1 KTBC: 1 the Agriculture Committee of the Senate: 1 Arthur Sylvester: 1 the Administrator of the Maritime Administration: 1 Harry Byrd: 1 the Committee on Reduction of Nonessential Federal Expenditures: 1 Byrd: 1 Charlotte Moton: 1 State for Public Affairs: 1 Hubbard: 1 Robert Moton: 1 Tuskegee Institute: 1 Chamizal Convention: 1 Chiari: 1 Illueca: 1 the Special Ambassadors: 1 U.S. Southern Command: 1 the Vanderburgh Humane Society of Evansville: 1 Ind.: 1 Dan Brewster: 1 Ed Foreman: 1 the House Armed Services Committee: 1 Erhard of: 1 the Appalachian States: 1 Hatcher: 1 Hart: 1 Congressmen Meader: 1 Goodby: 1 LeRoy Collins: 1 the Community Relations Service: 1 This Civil Rights Act: 1 RS-70: 1 J-58: 1 Segni: 1 the Federal Bureau: 1 Tshombe: 1 the Congolese Government: 1 moment--: 1 southeast Asian: 1 Tolley: 1 The Gulf of Tonkin: 1 the Declaration of Neutrality of Laos: 1 the Republic of Viet-Nam: 1 McKeithen: 1 AllenEllender: 1 Schiro: 1 CongressmanThompson: 1 Statewith: 1 Marshall Brown: 1 Donelon: 1 Senatorsinto: 1 withoutany pilon: 1 Washingtonup: 1 theMidwest: 1 the Deep South: 1 youremember: 1 Nationand: 1 The Federal Governmentexists: 1 Governmentwill: 1 Mason-Dixon: 1 Robert E.Lee: 1 ofLouisiana: 1 NewOrleans: 1 Queen City: 1 theSenate: 1 DemocraticCongress: 1 Lynda: 1 Ibecame: 1 NewEngland States: 1 Jack Kennedy: 1 Allen Ellender: 1 aBill of Rights: 1 yearsas: 1 allAmericans: 1 John FitzgeraldKennedy: 1 andDemocrat: 1 Andwe: 1 confronting us cross party: 1 the "Great Society: 1 Fullbright: 1 Billie Sol Estes: 1 Federal Housing Authority: 1 the Area Redevelopment Agency: 1 the Aid to Dependent Children Program: 1 Robert Byers: 1 Haile Selassie: 1 Chico County: 1 James Wier: 1 the Socialist Party: 1 the Party of Jefferson, Jackson: 1 Socialist Party of England: 1 An ex-GI: 1 Concord Bridge: 1 Barry Goldwater's: 1 Wild Rivers: 1 a White House Conference on Natural Beauty: 1 National Foundation on the Arts: 1 the Pedernales River: 1 the Union: Free and: 1 Administrator of Veterans Affairs: 1 Veterans Administrator: 1 the Veterans Administrator: 1 the Independent Offices Committee: 1 the House Veterans Committee: 1 Burkley: 1 the Senate Rules Committee: 1 Bundy.6: 1 Maxwell D. Taylor: 1 Ackley: 1 Biblical: 1 Otto Otepka: 1 Youth Corps: 1 Catoctin: 1 Camps: 1 Southern Senators: 1 Allow: 1 State of America: 1 Cotulla: 1 the LBJ Ranch: 1 George Wallace: 1 the Alabama Legislature: 1 LYNDON B. JOHNSON: 1 a State Government's: 1 Federal Service: 1 the National Government's: 1 Maxwell Taylor: 1 Henry Fowler: 1 Douglas Dillon: 1 John Macy: 1 Federal Cochairman: 1 the Appalachian Commission: 1 John L. Sweeney: 1 the Federal Development Planning Committee: 1 Charles R. Ross: 1 the Santa Fe Railroad: 1 Carl E. Bagge: 1 CAB: 1 John G. Adams: 1 the National Labor Relations Board: 1 Clifford Case: 1 Sam Zagoria: 1 the Yale Law School: 1 Silver Star Army: 1 the New York Young: 1 Stanley Resor: 1 Howard Woods: 1 the St. Louis Argus: 1 Carl T. Rowan: 1 Ranch: 1 the Associated Press luncheon: 1 National Guardsmen: 1 Ft. Bragg: 1 Craig Field: 1 Ft. Benning: 1 Henry Graham: 1 Guardsman: 1 Tuscaloosa: 1 James M. Wright: 1 H-43: 1 West German: 1 Driskill: 1 Grissom: 1 Project Ranger: 1 Bud Schurmeier: 1 Robert Seamans: 1 the Space Administration: 1 the Space Administrator: 1 the House Space Committee: 1 the Senate Space Committee: 1 Viola Liuzzo: 1 Eugene Thomas: 1 William Orville Eaton: 1 Gary Thomas Rowe: 1 Collie Leroy Wilkins: 1 Nicholas Katzenbach: 1 the United Klans of America, Inc.: 1 Knights of the Ku Klux Klan: 1 Liuzzo: 1 Klansmen: 1 the West Lobby: 1 United Steelworkers: 1 the union's committee: 1 Alan Boyd: 1 Commerce for Transportation: 1 Warren Wiggins: 1 Wiggins: 1 John A. Schnittker: 1 Iowa State University: 1 Agricultural Economics: 1 Charles S. Murphy: 1 Truman's Special Counsel: 1 William F. McKee: 1 the Administrator of the National Aeronautics and Space Administration: 1 the Federal Aviation Agency: 1 Wilbur J. Cohen: 1 Cohen: 1 Donald F. Turner: 1 Northwestern University: 1 the Antitrust Division: 1 Leonard C. Meeker: 1 Amherst College: 1 the U.S. Navy: 1 Haina: 1 Edward R. 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Labouisse: 1 the United Nations Children's Fund: 1 Raymond A. Hare: 1 Hare: 1 Penelope: 1 Hartland Thunberg: 1 the United States Tariff Commission: 1 Walter Schreiber: 1 Thunberg: 1 the International Division, Economic and Research Area: 1 Central Intelligence Agency: 1 Pembroke College: 1 M.A.: 1 Radcliffe College: 1 Disarmament Committee: 1 William C. Foster: 1 the Committee: 1 the Air Force's: 1 The Space Council: 1 Stennis: 1 Howard Smith: 1 Tuck: 1 Marines, Army: 1 the Government of South Viet-Nam: 1 Chancellor: 1 the National Broadcasting Company: 1 the Honorable Abe Fortas: 1 Donald Johnson: 1 The Viet Cong: 1 Gardner14: 1 Celebrezze: 1 Liberty Bell: 1 the States of Texas: 1 the Space Council: 1 the Douglas Aircraft Company: 1 The General Electric Company: 1 James Webb: 1 the Soviet Academy of Science: 1 Charles Yost: 1 James Nabrit: 1 the Economic and Social Council of the United Nations: 1 James Roosevelt: 1 the Trusteeship Council of the United Nations: 1 Eugenie Anderson: 1 the Public Works and Economic Development Act: 1 Administrator of the Small Business Administration: 1 Hobart Taylor: 1 the Board of Directors of: 1 the President's Committee on Equal Employment Opportunity: 1 Clifford Alexander: 1 David G. Bress: 1 American University: 1 Georgetown Law Center: 1 the University of Virginia Law School: 1 the Bar Association of the District of Columbia: 1 Prentiss: 1 Jefferson Davis County: 1 the House District Committee: 1 Bills: 1 the Federal Mediation Service: 1 William Simkin: 1 requires.6: 1 Ball: 1 Larry O'Brien: 1 the State of the Union for the third time: 1 Community Development Districts: 1 Teacher Corps: 1 a Highway Safety Act of 1966: 1 supreme association: 1 the International Education Act: 1 the International Health Act of 1966: 1 North to South: 1 the Geneva Agreements: 1 Armed: 1 the Southeast Asia Treaty: 1 John Foster Dulles: 1 The Europe of today: 1 Al Sorensen: 1 Phil Sorensen: 1 Callan: 1 nations--: 1 Alliance County: 1 the North Vietnamese Regular Army: 1 John Reed: 1 the National Governors' Conference: 1 Andy Goodpaster: 1 Walt Rostow: 1 Goodpaster: 1 Primary: 1 Clark Clifford: 1 Advisory Board on Foreign Intelligence: 1 Robert B. Bowie: 1 the Center for International Affairs: 1 Donald P. Lay: 1 8th Circuit: 1 Walter J. Cummings: 1 Thomas E. Fairchild: 1 Theodore Cabot: 1 Fort Lauderdale: 1 Wilfred Johnson: 1 General Electric's: 1 Hanford: 1 the Joint Committee on Atomic Energy: 1 the University of West Virginia: 1 Frank DiLuzio: 1 the Office of Saline Water: 1 Earle G. Wheeler: 1 Economic Advisers: 1 Selective: 1 Thomas Gates: 1 Oveta Culp Hobby: 1 WACS: 1 Halphong: 1 the American Alumni Council: 1 Buddhists: 1 East is East: 1 -Indonesia: 1 Pacific Council: 1 the Lower Mekong River Basin: 1 Malaysian: 1 Frank Cormier: 1 the International Committee of the Red Cross: 1
Merriman Smith: 1 Garnett Homer: 1 Washington Evening Star: 1 Wirtz: 1 Board of: 1 J. F. Ter Horst: 1 John Steele: 1 Marianne Means: 1 King Features Syndicate: 1 Sid Davis: 1 Westinghouse Broadcasting: 1 Peter Lisagor: 1 Chicago Daily News: 1 Forrest Boyd: 1 Mutual Broadcasting System: 1 Raymond L. Scherer: 1 Robert Pierpoint: 1 Ted Knap: 1 Scripps-Howard: 1 El Paso Times: 1 Larry Blackmon: 1 the Home Builders,7: 1 Robert G. Spivack: 1 Publishers Newspaper Syndicate: 1 Pat Brown: 1 Catherine Mackin: 1 John Scull: 1 the Geneva Convention of 1949: 1 Richard Wightman: 1 Fairchild Newspapers: 1 Women's Wear Daily: 1 malaria: 1 Sol M. Linowitz: 1 Xerox International, Inc.: 1 Linowitz: 1 Gordon: 1 Commerce for Economic Development: 1 Ross D. Davis: 1 the Economic Development Administration: 1 Open Arms: 1 the National Conference of Editorial Writers: 1 Title II: 1 Hearings: 1 the Republican Coordinating Committee: 1 National Committee: 1 Connor: 1 McClellan: 1 Holifield: 1 Congresswoman Dwyer: 1 Airplanes: 1 archaic equipment: 1 East Room: 1 This Department of Transportation: 1 the Maritime Administration: 1 the Department of Housing and Urban Affairs: 1 the Senate Commerce Committee: 1 Leaders Mansfield: 1 the African Development Bank: 1 Salisbury: 1 General McKee: 1 the Czechoslovakian Government: 1 The Democratic National Committee: 1 The Ways and Means Committee: 1 Arthur Schlesinger: 1 American Marines: 1 Podgorny: 1 Hershey: 1 Ted Sorenson: 1 Miss Means: 1 The Secretaries of Defense and Labor: 1 the Space Agency: 1 Gorrell: 1 Cummings: 1 Ellington: 1 the Southeast Asian Treaty: 1 General Westmoreland: 1 --The Vietnamese Army: 1 the South Vietnamese National Front for Liberation: 1 Constituent Assembly: 1 Eugene Locke: 1 klieg lights: 1 Selfridge Air Base: 1 Michigan National Guardsmen: 1 the Selfridge Air Force Base: 1 Michigan National Guard: 1 the State of Michigan: 1 Cavanagh of Detroit: 1 Selfridge Air Force Base: 1 Otto Kerner: 1 John Lindsay: 1 Fred R. Harris: 1 Edward W. Brooke: 1 James C. Corman: 1 22d District: 1 William M. McCulloch: 1 4th District: 1 I. W. Abel: 1 the United Steel Workers: 1 Charles B. Thornton: 1 Litton Industries: 1 Roy Wilkins: 1 Katherine Graham Peden: 1 Herbert Jenkins: 1 the State Governors: 1 Edgar Hoover: 1 the Continental Army Command: 1 Model Cities Act: 1 the Voters Rights Act: 1 the Civil Rights Acts: 1 the Rent Supplement Act: 1 the Safe Streets: 1 the Gun Control Act: 1 Harold K. Johnson: 1 First the Constituent Assembly: 1 Ky: 1 the North Vietnamese Government: 1 Larsen: 1 the II Corps: 1 the Minneapolis Tribune: 1 John Conyers: 1 the Housing and Urban Development: 1 Weaver: 1 Wood: 1 the House Foreign Affairs Committee: 1 the Armed Services Committee: 1 The Armed Services Committee: 1 Mahon: 1 Kazen: 1 Park of Korea: 1 First Corps: 1 Enterprise: 1 the Office of the President: 1 Summarizing: 1 Uncle Sam-whoever: 1 Byrnes: 1 Chair: 1 the Tonkin Gulf Resolution: 1 Champ Clark: 1 Woodrow Wilson's: 1 The Senate Foreign Relations Committee: 1 RATHER: 1 Hartke: 1 Scherer: 1 South---: 1 the South Vietnamese Army: 1 Kiesinger: 1 Dellinger: 1 Ray: 1 Kennedy-McCarthy: 1 the Geneva Conferences: 1 the Geneva Accords: 1 Southeast Asians: 1 the Mekong Valley: 1 Wasilewski: 1 Lowell Thomas: 1 Henry Adams: 1 the National Association of Broadcasters: 1 Pusan: 1 The Office of the Presidency: 1 Dick Daley: 1 the Outer Space Treaty: 1 the Governments of the Union of Soviet Socialist Republics: 1 the Democratic Parties: 1 the Hanoi Government: 1 Greatness: 1 Archibald MacLeish: 1 Hue: 1 Democratic Republic of Vietnam: 1 Vietnams: 1 Laird: 1 Cambodian Army: 1 the Cambodian Army: 1 Parrot: 1 World War I. Franklin Roosevelt: 1 Federal Reserve System: 1 Human Resources: 1 Community Development: 1 Economic Development: 1 Ho Chi Minh Trail: 1 Sergeant Karl Taylor: 1 Medal: 1 Jr.—he: 1 The Governments of the United States: 1 Inaugural Address: 1 Historically: 1 the Advisory Commission on Intergovernmental Relations: 1 Paris 12: 1 Pompidou of: 1 Huong: 1 wanton disregard of human: 1 North and South Vietnam: 1 the Nation of May 8: 1 the Shoreham Hotel: 1 Shoreham: 1 North, East: 1 Your National Government: 1 the Agreement on Ending the War and Restoring Peace: 1 the International Conference Center: 1 New Year's: 1 the Committee for the Re-Election: 1 Bob Haldeman: 1 John Sirica: 1 the Watergate: 1 Brandt of West Germany: 1 The Year of Europe: 1 the Democratic National Headquarters: 1 The White House Counsel: 1 the Criminal Division of the Justice Department: 1 Project Independence: 1 Ave: 1 Congress when America: 1 Lyndon B. 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Marjory Stoneman Douglas High School: 1 Meadow Pollack: 1 Meadow: 1 Peterson: 1 West Side Highway: 1 Foxconn: 1 Chris Sununu: 1 Sessions: 1 Jeff.—(applause)—Secretary Azar: 1 Nielsen: 1 Surgeon General Adams: 1 the Manchester Fire Department Safe Station: 1 Dan Goonan: 1 National Prescription Drug Take: 1 757s: 1 the Drug Enforcement Agents: 1 Operation Granite Shield: 1 Jean Mozer: 1 Jean: 1 East Kingston: 1 CrisisNextDoor.gov: 1 Dartmouth: 1 Derek Dunn: 1 Ron Morin: 1 Manchester Police Detective: 1 Patrick Maguire: 1 Jeff Sessions: 1 Mike Kelly: 1 Adapt Pharma’s: 1 Adapt: 1 Kaléo: 1 Eli Lilly: 1 Greg Walden: 1 Harman: 1 the Baseball Hall of Fame: 1 the Kansas City Royals: 1 good—.305: 1 Bob Wallace: 1 National Auxiliary: 1 Dee Guillory: 1 Dee: 1 BJ Lawrence: 1 Sandy Kreebull: 1 Robert Wilkie: 1 Acting VA: 1 Peter O’Rourke: 1 Vicky Hartzler: 1 Billy Long: 1 Jason Smith: 1 David Friedman: 1 jobs—(laughter)—“let: 1 George Washington’s: 1 F-35 Lightning: 1 Super Stealth: 1 the White House VA Hotline: 1 GI Bill: 1 Keep America Great: 1 PRESIDENT:—and: 1 PRESIDENT:—they’ve: 1 Allen Jones: 1 the Home of Brave: 1 the United States—(applause)—with: 1 JONES:—to: 1 Allen Q. Jones: 1 the Greatest Generation: 1 Bunker Hill: 1 the Inchon Landing: 1 Iran Nuclear Deal: 1 the U.S. Embassy: 1 U.S.-Mexico: 1 U.S.-Korea: 1 Nikki Haley: 1 the Human Rights Council: 1 Reliance: 1 Global Compact on Migration: 1 King Salman: 1 Presidents: 1 Angel moms: 1 Temporary Protected Status: 1 vineyards: 1 the Great Crusade: 1 Joseph Reilly: 1 Sergeant Irving Locker: 1 Sergeant Herman Zeitchik: 1 Apollo 11: 1 Farm Bill: 1 Alice Johnson: 1 First Step Act: 1 Matthew Charles: 1 cartels: 1 Dads: 1 Debra Bissell: 1 Debra: 1 Elvin Hernandez: 1 Law Enforcement: 1 the United States Reciprocal Trade Act: 1 Joining Melania: 1 Grace Eline: 1 St. Jude Children’s Research Hospital: 1 the Intermediate-Range Nuclear Forces Treaty: 1 INF Treaty: 1 Juan Guaido: 1 Tom Wibberley: 1 Craig Wibberley: 1 the Tree of Life Synagogue: 1 Timothy Matson: 1 Timothy: 1 Matson: 1 Judah Samet: 1 It’s the Americans: 1 Joshua Kaufman: 1 Dachau Concentration Camp: 1 Herman Zeitchik: 1 the golden beaches of: 1 Tijuana: 1 The Angel Moms: 1 Angel Dads: 1 Angel Families: 1 Trillions: 1 Ports: 1 the Angel Moms and Dads: 1 Marco Rubio: 1 Roberta Rampton: 1 ’s United States: 1 Laura Ingraham: 1 Tucker Carlson: 1 MSNBC: 1 Rasmussen —: 1 Jim Acosta: 1 the Angel Moms: 1 PARTICIPANT: 1 Kirstjen: 1 USCMA: 1 Combine: 1 Idlib Province: 1 Nobel Prize: 1 Micron Technology: 1 Hong Kong’s: 1 Fanatics: 1 Thankfully: 1 the Women’s Global Development and Prosperity Initiatives: 1 the U.N. 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Wyatt Earp: 1 Annie Oakley: 1 the Wild West: 1 the American Epic: 1 Los Angeles Times: 1 DNC: 1 Vindman: 1 Two Supreme Court: 1 Gillibrand: 1 Barrasso: 1 Mitt Romney: 1 Kelly Loeffler: 1 the Republican Party’s: 1 Donald Trump’s: 1 Apprentice: 1 Attorney: 1 n’t United Kingdom: 1 Kelly Armstrong: 1 Jim Banks: 1 Andy Biggs: 1 Honest Abe Lincoln: 1 Honest Abe: 1 Bradley Byrne: 1 Doug Collins: 1 Matt Gaetz: 1 Super Bowl: 1 Andy Reid: 1 Penn State: 1 Lesko: 1 Mike Johnson: 1 Devin Nunes: 1 Scott Perry: 1 Barr: 1 Barr first: 1 Hurt Roger: 1 the Nationals Park: 1 the New York Yankees: 1 Liz: 1 Michael Turner: 1 Andrew McCabe: 1 Strzok: 1 Muller: 1 G-Man: 1 Page: 1 Cryin’ Chuck: 1 Ivanka Trump: 1 Thermo Fisher: 1 Doug McMillon: 1 Doug and Walmart: 1 Brian Cornell: 1 Becton Dickinson: 1 Stephen Rusckowski: 1 Quest Diagnostics: 1 the Center for Disease Control: 1 Matt Sause: 1 David Pierre: 1 Signify: 1 Adam Schechter: 1 Thomas Moriarty: 1 Bruce Greenstein: 1 LHC Group: 1 Greenstein: 1 the U.S. 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Health or World Trade: 1 Bobbsey Twins: 1 Coronavirus Guidelines for America: 1 Phil Murphy: 1 Captain Crozier: 1 the White House Task Force: 1 I’ve: 1 metro: 1 Rockland: 1 Westchester: 1 Suffolk: 1 Bergen: 1 metro area: 1 mosaic: 1 Admiral Giroir: 1 Sub-Saharan Africa: 1 Washington D.C.: 1 DOD: 1 Owens &: 1 Minor: 1 Moldex: 1 Draeger: 1 Smithfield: 1 Del Rio: 1 Sheboygan Falls: 1 Aberdeen: 1 Abbott Laboratory: 1 Indian Health Service: 1 the Indian Health Service: 1 Project Air Bridge: 1 Kokomo: 1 the Federal Reserve Board of Governors: 1 Broadcasking: 1 Board of Governors: 1 Mike McFarland: 1 Tim Miller: 1 Coronavirus Guidelines: 1 The Mayo Clinic: 1 the Presidential Guidelines for America: 1 Article Two: 1 the Paycheck Plan: 1 Tim Arvier: 1 Channel 9 Australia: 1 Scott Morrison: 1 World Health Organization: 1 Tedros: 1 Health Care Enhancement Act: 1 DHS: 1 DHS Science and Technology: 1 Project Airbridge: 1 the Project Airbridge: 1 the Expanded Access Program: 1 Ben Carson: 1 the White House Opportunity and Revitalization Council: 1 Carson: 1 Thermo Fisher’s: 1 Tim Walz: 1 Kim Reynolds: 1 the Test Iowa Initiative: 1 Nomi Health: 1 the University of Iowa Hospitals: 1 Andy Beshear: 1 Greg Abbott: 1 Doug Ducey: 1 the Opening Up America Guidelines: 1 Guidelines for Opening Up America Again: 1 Kate Brown: 1 Brad Little: 1 Rebound Idaho: 1 the U.S. Department of Homeland Security: 1 S&T’s National Biodefense Analysis and Countermeasures Center: 1 Amerithrax: 1 the Coronavirus Task Force: 1 John Hopkin Applied Physics Lab: 1 the Master Questions List: 1 DHS S&T’s: 1 Gavin Newson: 1 we’ve—we’ve: 1 humid: 1 Dangerous: 1 Oxford University: 1 Oxford: 1 Stephen Hahn: 1 Hahn: 1 Boris: 1 The North Koreans: 1 PRESIDENT:—CNN: 1 Spas: 1 delta: 1 a Democrat Party: 1 George Floyd: 1 Upstate New York: 1 New York’s Finest: 1 the World War Two Memorial: 1 Darryl Williams: 1 the Army Rangers: 1 Airborne: 1 Steve Womack: 1 the Army Ryan McCarthy: 1 Casey Wardynski: 1 James McConville: 1 Curtis Buzzard: 1 Cindy Jebb: 1 West Point’s: 1 the U.S. Military’s: 1 the Long Gray Line: 1 Trophy: 1 Go, Army: 1 the Black Knights: 1 a Women’s Rugby Championship: 1 MVP: 1 Sam Sullivan: 1 Adaya Queen: 1 Brendan Brown: 1 Powerlifting National Champion: 1 Dane Van de Wall: 1 the Rhodes Scholarship: 1 Dane: 1 Lindy Mooradian: 1 the Battle of the Wilderness: 1 the Army Tank Corps: 1 the Pacific War: 1 People of the Philippines: 1 the American Armed Forces: 1 C.J. Morgan: 1 Christopher Morgan: 1 245th: 1 Constitution Island: 1 the United States Military Academy: 1 Kavanaugh: 1 the V.A. People: 1 AOC: 1 the Boston Tea Party: 1 Fifth Avenue: 1 Inslee: 1 747-800: 1 Cancel: 1 Jefferson Memorial: 1 Roger Goodell: 1 Joe Biden’s America: 1 ISIS al-Baghdadi: 1 the Logan Act: 1 Dems: 1 Alexandria Ocasio-Cortez: 1 Air Force Ones: 1 Ilhan Omar: 1 Trojan: 1 don’t-: 1 Matt Pinnell: 1 Frank Lucas: 1 Tom Cole: 1 Markwayne Mullin: 1 Kevin Hern: 1 Elise Stefanik: 1 Tom Emmer: 1 David Kustoff: 1 Greg Pence: 1 David Bernhardt: 1 the John Hope Franklin Reconciliation Park: 1 the African American Civil Rights Network: 1 Salute to America: 1 the U.S. Army: 1 The Golden Knights: 1 communists: 1 Marxists: 1 The National Garden of American Heroes: 1 Betsy Ross: 1 Dolly Madison: 1 Harriet Beecher Stowe: 1 Joshua Lawrence Chamberlain: 1 Daniel Boone: 1 Wilbur Wright: 1 George S. Patton: 1 Audie Murphy: 1 n’t America: 1 Fallen Officer: 1 David Dorn: 1 the St. Louis Police Department: 1 Air Traffic Control: 1 Veterans Accountability: 1 the Heroes Act: 1 the Bernie Sanders: 1 Bernie: 1 Democrat Party: 1 Crying Chuck Schumer: 1 The Department of Housing and Urban Development: 1 the Coronavirus Relief Fund: 1 Operation Warp Speed: 1 Friend: 1 the U.S. Treasury: 1 the ISIS caliphate—100 percent: 1 World Trade: 1 n’t kick: 1 The New York Times: 1 he’s: 1 John Bolton: 1 Christina Bobb: 1 One America News: 1 The U.S. Marshals: 1 Spunt: 1 David Spunt: 1 Bedminster: 1 Meadows: 1 David Jackson: 1 USA Today: 1 DeJoy: 1 GOP: 1 Navalny: 1 Angela Merkel: 1 Jeff Mason’s: 1 Darlene Superville: 1 Washington Monument: 1 Gorsuch: 1 Rhodes College: 1 the Law Review: 1 Laurence Silberman: 1 Amy Coney: 1 Notre Dame Law: 1 Down Syndrome: 1 the Circuit Court: 1 BARRETT: 1 Linda Coney: 1 Midwesterner: 1 Clarence Thomas: 1 Senate Judiciary: 1 Lindsey: 1 Marsha Blackburn: 1 Kevin Cramer: 1 Steve Daines: 1 Ron Johnson: 1 Martha McSally: 1 Pat Cipollone: 1 the U.S. Circuit Court of Appeals: 1 Civil Rights: 1 the Democratic Republic: 1 Quinnipiac: 1 the State Supreme Court: 1 The 11th Circuit: 1 covid-19: 1 capitol: 1 New Year’s Day 1863: 1 Bringing America: 1 Saint Augustine: 1 Lies: 1 American Anthem: 1 Driven: 1 Jared: 1 Eric: 1 Lara: 1 the United States Secret Service: 1 the White House Military Office: 1 Marine One: 1 America First: 1 Incomes: 1 VA Choice: 1 Kennedy Space Center: 1 al Baghdadi: 1 The Abraham Accords: 1 the Korean Assembly: 1 the Forbidden City: 1 Mount Rushmore: 1 Hemingway: 1 Weddings: 1 Vicious: 1 Moderna: 1 Merck: 1 K-8: 1 Miguel Cardona: 1 the Second Gentleman: 1 Perseverance: 1 Department of Education Department: 1 Title 42: 1 CPB: 1 Seung Min: 1 The Customs and Border Protection: 1 Donna: 1 Ken: 1 Remain: 1 U.N. Resolution 1718: 1 Kristen: 1 this White House: 1 Jim Eagle: 1 John Lewis’s: 1 Justin Sink: 1 Hu: 1 PAB: 1 anti-Chinese: 1 Quad: 1 South China Sea: 1 the North China Sea: 1 Uighurs: 1 Penn: 1 Univision: 1 Cecilia: 1 the Irish Sea: 1 Inaudible: 1 the County Executives: 1 this Carpenters Training Center: 1 Claymont: 1 Conor: 1 County: 1 Black, Latino: 1 American Jobs: 1 “Happy Birthday”: 1 the Protecting the Right to Organize Act: 1 World War Two and 2017: 1 Madam: 1 First Lady—(applause)—I’m: 1 Speaker Pelosi: 1 Community Health Centers: 1 Glendale: 1 the Rescue Plan: 1 The International Monetary Fund—(applause)—the International Monetary Fund: 1 the Protect the Right to Organize Act: 1 America—2 percent: 1 the Defense Advanced Research Project Agency: 1 The National Institute of Health: 1 Advanced Research Projects Agency for Health: 1 the Jobs Plan: 1 Historical Black Colleges: 1 Tribal Colleges: 1 Child Care Tax Credit: 1 The Affordable Care Act: 1 the Affordable Care: 1 the Cayman Islands: 1 George W.: 1 the New START Treaty: 1 Kunar Valley: 1 Gianna Floyd: 1 Pacific Islanders: 1 the Equality Act: 1 America—250: 1 the Department of Justice a-—actions: 1 H.R. 1: 1 In America: 1 Autocrats: 1 Lisa Blunt: 1 the Hall of Survivors: 1 Greenwood Cultural Center: 1 And—(applause)—I: 1 the Dreamland Theatre: 1 Stradford: 1 Mt. Zion: 1 A.J. Smitherton: 1 Smitherman: 1 Jon Meacham: 1 The Soul of America”—not: 1 D.C. Jesse: 1 Washin-—Washington: 1 ha-—or: 1 North Tulsa: 1 Black Tulsa: 1 Horsford: 1 Black American: 1 Ameri: 1 Black Caucus: 1 the People Act: 1 Neo-Nazis: 1 Jewish Americans: 1 John Hope: 1 Pat Caddell: 1 The Cure: 1 Heaney: 1 the Afghan National Security Force: 1 Afghan National Defense and Security Forces: 1 the NATO Allies: 1 mil-—is: 1 Staying: 1 Afghan National Security and Defense Forces: 1 Scott Miller: 1 the Afghan National Security Forces: 1 al Shabaab: 1 al-Nusra: 1 the Kunar Valley: 1 Operation Allies Refugee: 1 Special Immigration Visas: 1 the Book of Isaiah: 1 Mackenzie: 1 Aamer: 1 Afghani: 1 Bagram Air Force Base: 1 Dover Air Force Base: 1 Afghan National Security Forces: 1 Our State Department: 1 al-Shabaab: 1 Brown University: 1 pancreitis: 1 Disney: 1 Tysons Food: 1 the Centers for Disease Control: 1 The Department of Education: 1 Kroger’s: 1 the Transportation Safety Administration: 1 TSA: 1 COVID-19 Economic Injury Disaster Loan: 1 Iron Workers: 1 Nance: 1 Jill, Doug: 1 Rob Portman: 1 Rob: 1 Kyrsten Sinema: 1 Kyrsten: 1 Tom Carper: 1 Shelley Moore Capito: 1 Committee Chair: 1 Pete DeFazio: 1 the Business Roundtable: 1 the United Auto Workers: 1 NEA: 1 Machinists: 1 Hurricane Ida: 1 Queens: 1 the Bipartisan Infrastructure Bill: 1 Recovery Act: 1 “Sheriff Joe”: 1 Mitch Landrieu: 1 That’s the moment America: 1 the Senate Intelligence Committee: 1 Statuary Hall: 1 Recounts: 1 Capitol Rotunda: 1 Brian Sicknick: 1 Billy Evans: 1 Bloody Sunday: 1 the Edmund Pettus Bridge: 1 Dollars: 1 Euros: 1 Yen: 1 V.T.B.: 1 Milley: 1 Chris Megerian: 1 Wall Street Journal: 1 Tarina: 1 Cecilia Ve-—Vega: 1 Volodymyr Zelenskyy: 1 another Cold War: 1 the Defense Department of: 1 Groups: 1 World War 2: 1 the Russian Ruble: 1 The US Department of Justice: 1 Strategic Petroleum Reserve: 1 the Bipartisan Innovation Act: 1 Smartphones: 1 Pat Gelsinger: 1 history—$7 billion: 1 JoJo Burgess: 1 Rust Belt: 1 Seventeen Nobel: 1 Joshua Davis: 1 the Child Tax Credit: 1 the PRO Act: 1 Test to Treat: 1 Wilbert Mora: 1 Jason Rivera: 1 Rivera: 1 the Disclose Act: 1 Stephen Breyer: 1 Ketanji Brown Jackson: 1 the Fraternal Order of Police: 1 the US: 1 Frances Haugen: 1 Beau Biden: 1 Danielle Robinson: 1 Sergeant First Class: 1 Heath Robinson: 1 Army National Guard: 1 Ohio State: 1 Legos: 1 Cancer: 1 the Advanced Research Projects Agency for Health: 1 Pope: 1 Lech Wałęsa: 1 Kierkegaard: 1 Madeleine Korbel Albright: 1 Central Bank: 1 McDonald’s: 1 National Stadium: 1 José Andrés: 1 The World Food Programme: 1 Metropol: 1 fact—(applause)—Russia: 1 Alexei Kosygin: 1 the European Commission: 1 Pan-European Picnic: 1 Baltic Way: 1 Sandy Hook Elementary School: 1 Marjorie Stoneman: 1 Douglas High School: 1 Santa Fe High School: 1 Oxford High School: 1 Federal Circuit Court: 1
Michael Luttig: 1 Heartland: 1 wa-—on: 1 Izyum: 1 the Los Angeles Declaration of Migration and Protection: 1 U.S.-Africa: 1 the Paris Agreement: 1 Our Department of Energy: 1 PREPARE: 1 Preparedness: 1 the Seventh Replenishment Conference for the Global Fund: 1 Malaria: 1 Black Sea: 1 the World Food Programme: 1 UNICEF: 1 USAID: 1 the Trade and Technology Council: 1 the Indo-Pacific Economic Framework: 1 the Pacific Islands: 1 Paris Club: 1 a Partnership for Global Infrastructure and Investment: 1 Non-Proliferation: 1 NPT Review Conference: 1 the Democratic People’s Republic of Korea: 1 Xinjiang: 1 the Office of U.N.—U.S.—reports: 1 Associate Justices: 1 the 118th Congress: 1 Black House: 1 Hakeem Jeffries: 1 the Electoral Count Reform Act: 1 the Respect for Marriage Act: 1 Pride: 1 Science Act: 1 Urban: 1 Brent Spence: 1 Ironworkers Local 44: 1 Ohio River: 1 McDonald: 1 Insulin: 1 Historic: 1 Tyre Nichols: 1 Black and Brown: 1 the George Floyd Act: 1 Brandon Tsay: 1 Lunar New Year: 1 the People’s Republic of China: 1 the Heath Robinson PACT Act: 1 Fentanyl: 1 Fed Ex: 1 Big Tech: 1 Maurice: 1 Kindred: 1 Ava: 1 the Big Lie: 1 this House of Representatives: 1 Paul Pelosi: 1 Hello: 1 the NATOization of Finland: 1 the Warsaw Uprising: 1 Moldovan: 1 Sandu: 1 Kherson: 1 the Orange Revolution: 1 the Heavenly Hundred: 1
# Create a DataFrame from the 2D list
# If you have column names, replace ['Column1', 'Column2', 'Column3'] with your column names
enn = pd.DataFrame(ents, columns=['entied', 'countied'])
enn
| entied | countied | |
|---|---|---|
| 0 | the United States | 8285 |
| 1 | Congress | 8163 |
| 2 | American | 6278 |
| 3 | America | 4351 |
| 4 | Americans | 2724 |
| ... | ... | ... |
| 16093 | Moldovan | 1 |
| 16094 | Sandu | 1 |
| 16095 | Kherson | 1 |
| 16096 | the Orange Revolution | 1 |
| 16097 | the Heavenly Hundred | 1 |
16098 rows × 2 columns
enn.reset_index(inplace=True)
enn.drop('index', axis=1, inplace=True)
enn.to_csv('entites_common.csv', index=False)
enn = pd.read_csv("entites_common.csv")
enn
| entied | countied | |
|---|---|---|
| 0 | the United States | 8285 |
| 1 | Congress | 8163 |
| 2 | American | 6278 |
| 3 | America | 4351 |
| 4 | Americans | 2724 |
| ... | ... | ... |
| 16093 | Moldovan | 1 |
| 16094 | Sandu | 1 |
| 16095 | Kherson | 1 |
| 16096 | the Orange Revolution | 1 |
| 16097 | the Heavenly Hundred | 1 |
16098 rows × 2 columns
Bar Chart of count of first 50 most common Entites¶
# Entity names and their counts (first 50), can be seen in the entites_common.csv file
entities = {
"the United States": 8279,
"Congress": 8159,
"American": 6276,
"America": 4351,
"Americans": 2724,
"State": 2275,
"States": 1998,
"Constitution": 1869,
"Government": 1582,
"Senate": 1569,
"Treasury": 1481,
"Washington": 1156,
"Europe": 1037,
"Mexico": 978,
"China": 841,
"Navy": 777,
"Army": 760,
"Great Britain": 679,
"Cuba": 618,
"the Soviet Union": 613,
"British": 603,
"Iraq": 590,
"Spain": 587,
"Indian": 568,
"France": 520,
"the Federal Government": 516,
"Indians": 504,
"Texas": 497,
"Vietnam": 473,
"House": 472,
"Russia": 467,
"Afghanistan": 458,
"Soviet": 450,
"Executive": 442,
"the House of Representatives": 429,
"Republican": 429,
"Republicans": 427,
"the United Nations": 407,
"United States": 399,
"Democrats": 395,
"Department": 387,
"Japan": 387,
"Germany": 386,
"The United States": 382,
"Asia": 380,
"the United States of America": 367,
"European": 364,
"the Middle East": 363,
"Chinese": 361,
"Pacific": 357,
"Communist": 354
}
# Create lists for names and counts
names = list(entities.keys())
counts = list(entities.values())
# Create the histogram
plt.figure(figsize=(10, 8))
plt.barh(names, counts, color='skyblue')
plt.xlabel('Counts')
plt.ylabel('Entities')
plt.title('Histogram of Entity Mentions')
plt.gca().invert_yaxis() # Invert y-axis to have the highest count at the top
plt.show()
These took really long so i saved the data so you dont have to run it¶
'''
woah = pd.read_csv('entities_spec.csv')
listofem=[]
i=0
for ent in enn['entied']:
print(""+str(i)+'/'+str(len(enn))+"")
i=i+1
temp=woah.loc[woah['entity2']== ent]
now = pd.DataFrame(columns=['year', ent])
for date in dates:
if date in temp['year'].values:
ddd=temp.loc[temp["year"]== date]
ddd.reset_index(inplace=True)
ddd.drop('index', axis=1, inplace=True)
now.loc[len(now)] = [ date, int(ddd.loc[0,"county"])]
else:
now.loc[len(now)] = [ date, 0]
listofem.append(now)
letsgo = pd.DataFrame()
letsgo
for i in range(len(listofem)):
print(i)
letsgo= pd.concat([letsgo, listofem[i][listofem[i].columns[1]] ], axis=1)
letsgo.reset_index(inplace=True)
letsgo.drop('index', axis=1, inplace=True)
letsgo.to_csv('entities_counted.csv', index=False)
'''
'\nwoah = pd.read_csv(\'entities_spec.csv\')\nlistofem=[]\n\ni=0\nfor ent in enn[\'entied\']:\n print(""+str(i)+\'/\'+str(len(enn))+"")\n i=i+1\n temp=woah.loc[woah[\'entity2\']== ent]\n now = pd.DataFrame(columns=[\'year\', ent])\n for date in dates:\n if date in temp[\'year\'].values:\n ddd=temp.loc[temp["year"]== date]\n ddd.reset_index(inplace=True)\n ddd.drop(\'index\', axis=1, inplace=True)\n now.loc[len(now)] = [ date, int(ddd.loc[0,"county"])]\n else:\n now.loc[len(now)] = [ date, 0]\n listofem.append(now)\n \nletsgo = pd.DataFrame()\nletsgo\n\nfor i in range(len(listofem)):\n print(i)\n letsgo= pd.concat([letsgo, listofem[i][listofem[i].columns[1]] ], axis=1)\n\nletsgo.reset_index(inplace=True)\nletsgo.drop(\'index\', axis=1, inplace=True)\nletsgo.to_csv(\'entities_counted.csv\', index=False)\n\n'
letsgo = pd.read_csv("entities_counted.csv")
letsgo
| the United States | Congress | American | America | Americans | State | States | Constitution | Government | Senate | ... | this House of Representatives | Paul Pelosi | Hello | the NATOization of Finland | the Warsaw Uprising | Moldovan | Sandu | Kherson | the Orange Revolution | the Heavenly Hundred | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 3 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 22 | 3 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 3 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 14 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 2 | 2 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 4 | 4 | 0 | 0 | 0 | 0 | 3 | 4 | 1 | 2 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 18 | 6 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 2 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 230 | 53 | 26 | 54 | 47 | 28 | 0 | 1 | 1 | 0 | 4 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 231 | 80 | 47 | 212 | 138 | 123 | 0 | 1 | 19 | 0 | 20 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 232 | 54 | 25 | 213 | 177 | 136 | 3 | 0 | 6 | 0 | 23 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 233 | 46 | 12 | 60 | 91 | 45 | 1 | 0 | 11 | 0 | 4 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 234 | 8 | 11 | 26 | 39 | 19 | 0 | 0 | 0 | 0 | 2 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
235 rows × 16098 columns
# Re-creating the DataFrame with U.S. Presidents and their party affiliations
presidents_df = pd.DataFrame({
"President": ["George Washington", "John Adams", "Thomas Jefferson", "James Madison",
"James Monroe", "John Quincy Adams", "Andrew Jackson", "Martin Van Buren",
"William Henry Harrison", "John Tyler", "James K. Polk", "Zachary Taylor",
"Millard Fillmore", "Franklin Pierce", "James Buchanan", "Abraham Lincoln",
"Andrew Johnson", "Ulysses S. Grant", "Rutherford B. Hayes", "James A. Garfield",
"Chester A. Arthur", "Grover Cleveland", "Benjamin Harrison", "Grover Cleveland",
"William McKinley", "Theodore Roosevelt", "William Howard Taft", "Woodrow Wilson",
"Warren G. Harding", "Calvin Coolidge", "Herbert Hoover", "Franklin D. Roosevelt",
"Harry S. Truman", "Dwight D. Eisenhower", "John F. Kennedy", "Lyndon B. Johnson",
"Richard Nixon", "Gerald Ford", "Jimmy Carter", "Ronald Reagan",
"George H. W. Bush", "Bill Clinton", "George W. Bush", "Barack Obama",
"Donald Trump", "Joe Biden"],
"Start Year": [1789, 1797, 1801, 1809, 1817, 1825, 1829, 1837, 1841, 1841, 1845, 1849,
1850, 1853, 1857, 1861, 1865, 1869, 1877, 1881, 1881, 1885, 1889, 1893,
1897, 1901, 1909, 1913, 1921, 1923, 1929, 1933, 1945, 1953, 1961, 1963,
1969, 1974, 1977, 1981, 1989, 1993, 2001, 2009, 2017, 2021],
"End Year": [1797, 1801, 1809, 1817, 1825, 1829, 1837, 1841, 1841, 1845, 1849, 1850,
1853, 1857, 1861, 1865, 1869, 1877, 1881, 1881, 1885, 1889, 1893, 1897,
1901, 1909, 1913, 1921, 1923, 1929, 1933, 1945, 1953, 1961, 1963, 1969,
1974, 1977, 1981, 1989, 1993, 2001, 2009, 2017, 2021, 2025], # Assuming Biden's term ends in 2025
"Party": ["None (Independent)", "Federalist", "Democratic-Republican", "Democratic-Republican",
"Democratic-Republican", "Democratic-Republican", "Democrat", "Democrat",
"Whig", "Whig/None (Independent)", "Democrat", "Whig", "Whig", "Democrat",
"Democrat", "Republican", "Democratic/National Union", "Republican", "Republican",
"Republican", "Republican", "Democrat", "Republican", "Democrat", "Republican",
"Republican", "Republican", "Democrat", "Republican", "Republican", "Republican",
"Democrat", "Democrat", "Republican", "Democrat", "Democrat", "Republican",
"Republican", "Democrat", "Republican", "Republican", "Democrat", "Republican",
"Democrat", "Republican", "Democrat"]
})
# Now creating the year-by-year DataFrame
yearly_data = []
# Iterate over each year from 1789 to 2023
for year in range(1789, 2024):
# Find the president in office for that year
president_in_office = presidents_df[(presidents_df['Start Year'] <= year) & (presidents_df['End Year'] > year)]
if not president_in_office.empty:
president_name = president_in_office.iloc[0]['President']
party = president_in_office.iloc[0]['Party']
else:
# Continuing from where the previous code left off to complete the DataFrame creation
president_name = None
party = None
yearly_data.append({"Year": year, "President": president_name, "Party": party})
# Convert the list to a DataFrame
yearly_presidents_df = pd.DataFrame(yearly_data)
# Display the first few rows to check the result
yearly_presidents_df.head()
| Year | President | Party | |
|---|---|---|---|
| 0 | 1789 | George Washington | None (Independent) |
| 1 | 1790 | George Washington | None (Independent) |
| 2 | 1791 | George Washington | None (Independent) |
| 3 | 1792 | George Washington | None (Independent) |
| 4 | 1793 | George Washington | None (Independent) |
letsgo
| the United States | Congress | American | America | Americans | State | States | Constitution | Government | Senate | ... | this House of Representatives | Paul Pelosi | Hello | the NATOization of Finland | the Warsaw Uprising | Moldovan | Sandu | Kherson | the Orange Revolution | the Heavenly Hundred | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 3 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 22 | 3 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 3 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 14 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 2 | 2 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 4 | 4 | 0 | 0 | 0 | 0 | 3 | 4 | 1 | 2 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 18 | 6 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 2 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 230 | 53 | 26 | 54 | 47 | 28 | 0 | 1 | 1 | 0 | 4 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 231 | 80 | 47 | 212 | 138 | 123 | 0 | 1 | 19 | 0 | 20 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 232 | 54 | 25 | 213 | 177 | 136 | 3 | 0 | 6 | 0 | 23 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 233 | 46 | 12 | 60 | 91 | 45 | 1 | 0 | 11 | 0 | 4 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 234 | 8 | 11 | 26 | 39 | 19 | 0 | 0 | 0 | 0 | 2 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
235 rows × 16098 columns
# Applying the function to each year
letsgo.insert(0, '_Party_', yearly_presidents_df['Party'])
letsgo
| _Party_ | the United States | Congress | American | America | Americans | State | States | Constitution | Government | ... | this House of Representatives | Paul Pelosi | Hello | the NATOization of Finland | the Warsaw Uprising | Moldovan | Sandu | Kherson | the Orange Revolution | the Heavenly Hundred | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | None (Independent) | 3 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | None (Independent) | 22 | 3 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | None (Independent) | 14 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 2 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | None (Independent) | 4 | 4 | 0 | 0 | 0 | 0 | 3 | 4 | 1 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | None (Independent) | 18 | 6 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 230 | Republican | 53 | 26 | 54 | 47 | 28 | 0 | 1 | 1 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 231 | Republican | 80 | 47 | 212 | 138 | 123 | 0 | 1 | 19 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 232 | Democrat | 54 | 25 | 213 | 177 | 136 | 3 | 0 | 6 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 233 | Democrat | 46 | 12 | 60 | 91 | 45 | 1 | 0 | 11 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 234 | Democrat | 8 | 11 | 26 | 39 | 19 | 0 | 0 | 0 | 0 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
235 rows × 16099 columns
letsgo.insert(0, '_Year_', dates)
letsgo
| _Year_ | _Party_ | the United States | Congress | American | America | Americans | State | States | Constitution | ... | this House of Representatives | Paul Pelosi | Hello | the NATOization of Finland | the Warsaw Uprising | Moldovan | Sandu | Kherson | the Orange Revolution | the Heavenly Hundred | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1789 | None (Independent) | 3 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 1790 | None (Independent) | 22 | 3 | 0 | 0 | 0 | 1 | 1 | 1 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 1791 | None (Independent) | 14 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 1792 | None (Independent) | 4 | 4 | 0 | 0 | 0 | 0 | 3 | 4 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 1793 | None (Independent) | 18 | 6 | 0 | 0 | 0 | 0 | 2 | 1 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 230 | 2019 | Republican | 53 | 26 | 54 | 47 | 28 | 0 | 1 | 1 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 231 | 2020 | Republican | 80 | 47 | 212 | 138 | 123 | 0 | 1 | 19 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 232 | 2021 | Democrat | 54 | 25 | 213 | 177 | 136 | 3 | 0 | 6 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 233 | 2022 | Democrat | 46 | 12 | 60 | 91 | 45 | 1 | 0 | 11 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 234 | 2023 | Democrat | 8 | 11 | 26 | 39 | 19 | 0 | 0 | 0 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
235 rows × 16100 columns
letsgo.to_csv('entities_counted_Partied.csv', index=False)
letsgo
| _Year_ | _Party_ | the United States | Congress | American | America | Americans | State | States | Constitution | ... | this House of Representatives | Paul Pelosi | Hello | the NATOization of Finland | the Warsaw Uprising | Moldovan | Sandu | Kherson | the Orange Revolution | the Heavenly Hundred | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1789 | None (Independent) | 3 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 1790 | None (Independent) | 22 | 3 | 0 | 0 | 0 | 1 | 1 | 1 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 1791 | None (Independent) | 14 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 1792 | None (Independent) | 4 | 4 | 0 | 0 | 0 | 0 | 3 | 4 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 1793 | None (Independent) | 18 | 6 | 0 | 0 | 0 | 0 | 2 | 1 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 230 | 2019 | Republican | 53 | 26 | 54 | 47 | 28 | 0 | 1 | 1 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 231 | 2020 | Republican | 80 | 47 | 212 | 138 | 123 | 0 | 1 | 19 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 232 | 2021 | Democrat | 54 | 25 | 213 | 177 | 136 | 3 | 0 | 6 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 233 | 2022 | Democrat | 46 | 12 | 60 | 91 | 45 | 1 | 0 | 11 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 234 | 2023 | Democrat | 8 | 11 | 26 | 39 | 19 | 0 | 0 | 0 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
235 rows × 16100 columns
letsgo = pd.read_csv("entities_counted_Partied.csv")
letsgo
| _Year_ | _Party_ | the United States | Congress | American | America | Americans | State | States | Constitution | ... | this House of Representatives | Paul Pelosi | Hello | the NATOization of Finland | the Warsaw Uprising | Moldovan | Sandu | Kherson | the Orange Revolution | the Heavenly Hundred | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1789 | None (Independent) | 3 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 1790 | None (Independent) | 22 | 3 | 0 | 0 | 0 | 1 | 1 | 1 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 1791 | None (Independent) | 14 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 1792 | None (Independent) | 4 | 4 | 0 | 0 | 0 | 0 | 3 | 4 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 1793 | None (Independent) | 18 | 6 | 0 | 0 | 0 | 0 | 2 | 1 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 230 | 2019 | Republican | 53 | 26 | 54 | 47 | 28 | 0 | 1 | 1 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 231 | 2020 | Republican | 80 | 47 | 212 | 138 | 123 | 0 | 1 | 19 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 232 | 2021 | Democrat | 54 | 25 | 213 | 177 | 136 | 3 | 0 | 6 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 233 | 2022 | Democrat | 46 | 12 | 60 | 91 | 45 | 1 | 0 | 11 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 234 | 2023 | Democrat | 8 | 11 | 26 | 39 | 19 | 0 | 0 | 0 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
235 rows × 16100 columns
Chart of alot of Entites, not final product, just checking (charts take time to be made)¶
# Assuming df is your DataFrame
# Initialize a Min-Max Scaler
scaler = MinMaxScaler()
letgos = letsgo.drop(columns=letsgo.columns[0])
letgos = letgos.drop(columns=letgos.columns[0])
# Scale the data
letgos_scaled = pd.DataFrame(scaler.fit_transform(letgos), columns=letgos.columns, index=letsgo["_Year_"])
# Create the heatmap
plt.figure(figsize=(50, 100))
sns.heatmap(letgos_scaled.T, annot=False, cmap='Blues')
plt.show()
Chart 1¶
data = [
"Europe: 1037", "Germany: 386", "Hitler: 56", "Jews: 34",
"World War II: 157", "Ukraine: 137", "Turkey: 134"
]
# Splitting each string at ':' and taking the first part
cleaned_list_Europe = [(item.split(':')[0] )for item in data]
##
##
##
non_democrat_df = letsgo[letsgo['_Party_'] != 'Democrat']
non_Republican_df = letsgo[letsgo['_Party_'] != 'Republican']
import matplotlib.pyplot as plt
# Assuming non_democrat_df is your DataFrame
subs = cleaned_list_Europe
# Calculate the sum of each column
sums = []
for column in subs:
sums.append(non_democrat_df[column].sum())
# Create a bar chart using the sums
plt.bar(subs, sums, color='Red')
plt.xlabel('Entites', fontsize=20)
plt.ylabel('Sum', fontsize=20)
plt.title('World War Entites Sum Non Democrat' , fontsize=20)
plt.xticks(rotation=45, fontsize=15) # Rotate the x labels for better readability
plt.yticks( fontsize=15)
# Add a caption
plt.figtext(0.5, -0.4, "The bar chart displays the frequency of specific entities mentioned in all Republicn President Speeches since US presidency specific to World Wars.", wrap=True, horizontalalignment='center', fontsize=13)
plt.show()
# Calculate the sum of each column
sums = []
for column in subs:
sums.append(non_Republican_df[column].sum())
# Create a bar chart using the sums
plt.bar(subs, sums, color='Blue')
plt.xlabel('Entites', fontsize=20 )
plt.ylabel('Sum', fontsize=20)
plt.xticks(rotation=45, fontsize=15) # Rotate the x labels for better readability
plt.yticks( fontsize=15)
plt.title('World War Entites Sum Non Republican')
# Add a caption
plt.figtext(0.5, -0.4, "The bar chart displays the frequency of specific entities mentioned in all Democrat President Speeches since US presidency specific to World Wars.", wrap=True, horizontalalignment='center', fontsize=13)
plt.show()
def map_to_interval(year):
return f"{(year // 10) * 10}-{(year // 10) * 10 + 9}"
subs=non_democrat_df[cleaned_list_Europe]
# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
# Group by interval and sum
result_df = subs.groupby('Interval').sum()
result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)
# Set the size of the heatmap
plt.figure(figsize=(25, 7)) # You can adjust the size as needed
# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Reds') # 'annot=False' hides the data values, 'cmap' sets the color map
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('World War Speech Entities Over The Years Non Democrat\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.1, "The heatmap displays the frequency of specific entities mentioned in all Non-Democrat, including whips and non denomination parties, President Speeches since US presidency specific to World Wars in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
subs=non_Republican_df[cleaned_list_Europe]
# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
# Group by interval and sum
result_df = subs.groupby('Interval').sum()
result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)
# Set the size of the heatmap
plt.figure(figsize=(25, 7)) # You can adjust the size as needed
# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Blues') # 'annot=False' hides the data values, 'cmap' sets the color map
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('World War Speech Entities Over The Years Non Republican\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.1, "The heatmap displays the frequency of specific entities mentioned in all Non-Republican, including whips and non denomination parties, President Speeches since US presidency specific to World Wars in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
##
##
##
subs=letsgo[cleaned_list_Europe]
def map_to_interval(year):
return f"{(year // 10) * 10}-{(year // 10) * 10 + 9}"
# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
# Group by interval and sum
result_df = subs.groupby('Interval').sum()
result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)
# Set the size of the heatmap
plt.figure(figsize=(25, 7)) # You can adjust the size as needed
# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Purples') # 'annot=False' hides the data values, 'cmap' sets the color map
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('World War Speech Entities Over The Years\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.1, "The heatmap displays the frequency of specific entities mentioned in all President Speeches since US presidency specific to World Wars in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
# Assuming df is your DataFrame
# Initialize a Min-Max Scaler
scaler = MinMaxScaler()
# Scale the data
subs_scaled = pd.DataFrame(scaler.fit_transform(result_df), columns=result_df.columns, index=result_df.index)
# Create the heatmap
plt.figure(figsize=(25,7))
sns.heatmap(subs_scaled.T, annot=False, cmap='Purples')
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('World War Speech Entities Over The Years MinMax Scaled\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.1, "The heatmap displays the MinMax Scaled frequency of specific entities mentioned in all President Speeches since US presidency specific to World Wars in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
# Initialize a Standard Scaler
scaler = StandardScaler()
# Scale the data
letsgo_standardized = pd.DataFrame(scaler.fit_transform(result_df), columns=result_df.columns, index=result_df.index)
# Create the heatmap
plt.figure(figsize=(25, 7))
sns.heatmap(letsgo_standardized.T, annot=False, cmap='Purples')
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('World War Speech Entities Over The Years Standardized\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.1, "The heatmap displays the Standardized frequency of specific entities mentioned in all President Speeches since US presidency specific to World Wars in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2528904079.py:67: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2528904079.py:101: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2528904079.py:139: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
chart 2¶
data = [
"Cuba: 618", "Panama: 261", "Mexico: 978", "Nicaragua: 224",
"Cuban: 174", "the Panama Canal: 73", "Porto Rico: 72"
]
# Splitting each string at ':' and taking the first part
cleaned_list_South_American = [(item.split(':')[0] )for item in data]
##
##
##
non_democrat_df = letsgo[letsgo['_Party_'] != 'Democrat']
non_Republican_df = letsgo[letsgo['_Party_'] != 'Republican']
import matplotlib.pyplot as plt
# Assuming non_democrat_df is your DataFrame
subs = cleaned_list_South_American
# Calculate the sum of each column
sums = []
for column in subs:
sums.append(non_democrat_df[column].sum())
# Create a bar chart using the sums
plt.bar(subs, sums, color='Red')
plt.xlabel('Entities', fontsize=20 )
plt.ylabel('Sum', fontsize=20)
plt.xticks(rotation=75, fontsize=15) # Rotate the x labels for better readability
plt.yticks( fontsize=15)
plt.title('South American Entites Sum Non Democrat', fontsize =20)
# Add a caption
plt.figtext(0.5, -0.6, "The bar chart displays the frequency of specific entities mentioned in all Republican President Speeches since US presidency specific to South American Involvement.", wrap=True, horizontalalignment='center', fontsize=13)
plt.show()
# Calculate the sum of each column
sums = []
for column in subs:
sums.append(non_Republican_df[column].sum())
# Create a bar chart using the sums
plt.bar(subs, sums, color='Blue')
plt.xlabel('Entities', fontsize=20 )
plt.ylabel('Sum', fontsize=20)
plt.xticks(rotation= 75, fontsize=15) # Rotate the x labels for better readability
plt.yticks( fontsize=15)
plt.title('South American Entites Sum Non Republican', fontsize = 20)
# Add a caption
plt.figtext(0.5, -0.6, "The bar chart displays the frequency of specific entities mentioned in all Democrat President Speeches since US presidency specific to South American Involvement.", wrap=True, horizontalalignment='center', fontsize=13)
plt.show()
def map_to_interval(year):
return f"{(year // 10) * 10}-{(year // 10) * 10 + 9}"
subs=non_democrat_df[cleaned_list_South_American]
# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
# Group by interval and sum
result_df = subs.groupby('Interval').sum()
result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)
# Set the size of the heatmap
plt.figure(figsize=(25, 7)) # You can adjust the size as needed
# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Reds') # 'annot=False' hides the data values, 'cmap' sets the color map
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('South American Speech Entities Over The Years Non Democrat\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.1, "The heatmap displays the frequency of specific entities mentioned in all Non-Democratic, including whips and non denomination parties, President Speeches since US presidency specific to South American Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
subs=non_Republican_df[cleaned_list_South_American]
# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
# Group by interval and sum
result_df = subs.groupby('Interval').sum()
result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)
# Set the size of the heatmap
plt.figure(figsize=(25, 7)) # You can adjust the size as needed
# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Blues') # 'annot=False' hides the data values, 'cmap' sets the color map
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('South American Speech Entities Over The Years Non Republican\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.1, "The heatmap displays the frequency of specific entities mentioned in all Non-Republican, including whips and non denomination parties, President Speeches since US presidency specific to South American Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
##
##
##
subs=letsgo[cleaned_list_South_American]
def map_to_interval(year):
return f"{(year // 10) * 10}-{(year // 10) * 10 + 9}"
# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
# Group by interval and sum
result_df = subs.groupby('Interval').sum()
result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)
# Set the size of the heatmap
plt.figure(figsize=(25, 7)) # You can adjust the size as needed
# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Purples') # 'annot=False' hides the data values, 'cmap' sets the color map
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('South American Speech Entities Over The Years\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.1, "The heatmap displays the frequency of specific entities mentioned in all President Speeches since US presidency specific to South American Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
# Assuming df is your DataFrame
# Initialize a Min-Max Scaler
scaler = MinMaxScaler()
# Scale the data
subs_scaled = pd.DataFrame(scaler.fit_transform(result_df), columns=result_df.columns, index=result_df.index)
# Create the heatmap
plt.figure(figsize=(25,7))
sns.heatmap(subs_scaled.T, annot=False, cmap='Purples')
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('South American Speech Entities Over The Years MinMax Scaled\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.1, "The heatmap displays the MinMax Scaled frequency of specific entities mentioned in all President Speeches since US presidency specific to South American Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
# Initialize a Standard Scaler
scaler = StandardScaler()
# Scale the data
letsgo_standardized = pd.DataFrame(scaler.fit_transform(result_df), columns=result_df.columns, index=result_df.index)
# Create the heatmap
plt.figure(figsize=(25, 7))
sns.heatmap(letsgo_standardized.T, annot=False, cmap='Purples')
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('South American Speech Entities Over The Years Standardized\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.1, "The heatmap displays the Standardized frequency of specific entities mentioned in all President Speeches since US presidency specific to South American Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/3754587022.py:68: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/3754587022.py:102: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/3754587022.py:137: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
Chart 3¶
data = [
"the Soviet Union: 613", "Russia: 467", "Soviet: 450",
"Communist: 354", "Soviets: 177", "Communists: 175"
]
# Splitting each string at ':' and taking the first part
# Splitting each string at ':' and taking the first part
cleaned_list_russian = [item.split(':')[0] for item in data]
##
##
##
non_democrat_df = letsgo[letsgo['_Party_'] != 'Democrat']
non_Republican_df = letsgo[letsgo['_Party_'] != 'Republican']
import matplotlib.pyplot as plt
# Assuming non_democrat_df is your DataFrame
subs = cleaned_list_russian
# Calculate the sum of each column
sums = []
for column in subs:
sums.append(non_democrat_df[column].sum())
# Create a bar chart using the sums
plt.bar(subs, sums, color='Red')
plt.xlabel('Entites', fontsize=20 )
plt.ylabel('Sum', fontsize=20)
plt.xticks(rotation=75, fontsize=15) # Rotate the x labels for better readability
plt.yticks( fontsize=15)
plt.title('Russian Entites Sum Non Democrat', fontsize=20)
# Add a caption
plt.figtext(0.5, -0.55, "The bar chart displays the frequency of specific entities mentioned in all Republican President Speeches since US presidency specific to Russian Involvements.", wrap=True, horizontalalignment='center', fontsize=13)
plt.show()
# Calculate the sum of each column
sums = []
for column in subs:
sums.append(non_Republican_df[column].sum())
# Create a bar chart using the sums
plt.bar(subs, sums, color='Blue')
plt.xlabel('Entites', fontsize=20 )
plt.ylabel('Sum', fontsize=20)
plt.xticks(rotation=75, fontsize=15) # Rotate the x labels for better readability
plt.yticks( fontsize=15)
plt.title('Russian Entites Sum Non Republican', fontsize= 20)
# Add a caption
plt.figtext(0.5, -0.55, "The bar chart displays the frequency of specific entities mentioned in all Democrat President Speeches since US presidency specific to Russian Involvement.", wrap=True, horizontalalignment='center', fontsize=13)
plt.show()
def map_to_interval(year):
return f"{(year // 10) * 10}-{(year // 10) * 10 + 9}"
subs=non_democrat_df[cleaned_list_russian]
# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
# Group by interval and sum
result_df = subs.groupby('Interval').sum()
result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)
# Set the size of the heatmap
plt.figure(figsize=(25, 7)) # You can adjust the size as needed
# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Reds') # 'annot=False' hides the data values, 'cmap' sets the color map
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Russian Speech Entities Over The Years Non Democrat\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.1, "The heatmap displays the frequency of specific entities mentioned in all Non-Democratic, including whips and non denomination parties, President Speeches since US presidency specific to Cold War Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
subs=non_Republican_df[cleaned_list_russian]
# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
# Group by interval and sum
result_df = subs.groupby('Interval').sum()
result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)
# Set the size of the heatmap
plt.figure(figsize=(25, 7)) # You can adjust the size as needed
# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Blues') # 'annot=False' hides the data values, 'cmap' sets the color map
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Russian Speech Entities Over The Years Non Republican\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.1, "The heatmap displays the frequency of specific entities mentioned in all Non-Republican, including whips and non denomination parties, President Speeches since US presidency specific to Cold War Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
##
##
##
subs=letsgo[cleaned_list_russian]
def map_to_interval(year):
return f"{(year // 10) * 10}-{(year // 10) * 10 + 9}"
# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
# Group by interval and sum
result_df = subs.groupby('Interval').sum()
result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)
# Set the size of the heatmap
plt.figure(figsize=(25, 7)) # You can adjust the size as needed
# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Purples') # 'annot=False' hides the data values, 'cmap' sets the color map
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Russian Speech Entities Over The Years\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.1, "The heatmap displays the frequency of specific entities mentioned in all President Speeches since US presidency specific to Russian Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
# Assuming df is your DataFrame
# Initialize a Min-Max Scaler
scaler = MinMaxScaler()
# Scale the data
subs_scaled = pd.DataFrame(scaler.fit_transform(result_df), columns=result_df.columns, index=result_df.index)
# Create the heatmap
plt.figure(figsize=(25,7))
sns.heatmap(subs_scaled.T, annot=False, cmap='Purples')
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Russian Speech Entities Over The Years MinMax Scaled\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.1, "The heatmap displays the MinMax Scaled frequency of specific entities mentioned in all President Speeches since US presidency specific to Russian Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
# Initialize a Standard Scaler
scaler = StandardScaler()
# Scale the data
letsgo_standardized = pd.DataFrame(scaler.fit_transform(result_df), columns=result_df.columns, index=result_df.index)
# Create the heatmap
plt.figure(figsize=(25, 7))
sns.heatmap(letsgo_standardized.T, annot=False, cmap='Purples')
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Russian Speech Entities Over The Years Standardized\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.1, "The heatmap displays the Standardized frequency of specific entities mentioned in all President Speeches since US presidency specific to Russian Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/170823032.py:71: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/170823032.py:105: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/170823032.py:141: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
Chart 4¶
data = [
"Iraq: 590", "the Middle East: 363", "Afghanistan: 458", "Iran: 282",
"Iraqi: 191", "Taliban: 118", "Afghan: 112", "al Qaeda: 106",
"Arab: 99", "Saddam Hussein: 75", "Saddam Hussein's: 13",
"Iraqis: 75", "Somalia: 73", "Muslim: 48", "Al Qaeda: 45",
"Osama bin Laden: 30", "Afghans: 30", "Assad: 30"
]
# Splitting each string at ':' and taking the first part
# Splitting each string at ':' and taking the first part
cleaned_list_Middle_east = [item.split(':')[0] for item in data]
##
##
##
non_democrat_df = letsgo[letsgo['_Party_'] != 'Democrat']
non_Republican_df = letsgo[letsgo['_Party_'] != 'Republican']
import matplotlib.pyplot as plt
# Assuming non_democrat_df is your DataFrame
subs = cleaned_list_Middle_east
# Calculate the sum of each column
sums = []
for column in subs:
sums.append(non_democrat_df[column].sum())
# Create a bar chart using the sums
plt.bar(subs, sums, color='Red')
plt.xlabel('Entites', fontsize=20 )
plt.ylabel('Sum', fontsize=20)
plt.xticks(rotation=90, fontsize=15) # Rotate the x labels for better readability
plt.yticks( fontsize=15)
plt.title('Middle East Entites Sum Non Democrat', fontsize= 20)
# Add a caption
plt.figtext(0.5, -0.6, "The bar chart displays the frequency of specific entities mentioned in all Republican President Speeches since US presidency specific to Middle Eastern Involvement.", wrap=True, horizontalalignment='center', fontsize=13)
plt.show()
# Calculate the sum of each column
sums = []
for column in subs:
sums.append(non_Republican_df[column].sum())
# Create a bar chart using the sums
plt.bar(subs, sums, color='Blue')
plt.xlabel('Entites', fontsize=20 )
plt.ylabel('Sum', fontsize=20)
plt.xticks(rotation=90, fontsize=15) # Rotate the x labels for better readability
plt.yticks( fontsize=15)
plt.title('Middle East Entites Sum Non Republican', fontsize= 20)
# Add a caption
plt.figtext(0.5, -0.6, "The bar chart displays the frequency of specific entities mentioned in all Democrat President Speeches since US presidency specific to Middle Eastern Involvement.", wrap=True, horizontalalignment='center', fontsize=13)
plt.show()
def map_to_interval(year):
return f"{(year // 10) * 10}-{(year // 10) * 10 + 9}"
subs=non_democrat_df[cleaned_list_Middle_east]
# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
# Group by interval and sum
result_df = subs.groupby('Interval').sum()
result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)
# Set the size of the heatmap
plt.figure(figsize=(25, 10)) # You can adjust the size as needed
# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Reds') # 'annot=False' hides the data values, 'cmap' sets the color map
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Middle East Speech Entities Over The Years Non Democrat\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the frequency of specific entities mentioned in all Non-Democratic, including whips and non denomination parties, President Speeches since US presidency specific to Middle Eastern Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
subs=non_Republican_df[cleaned_list_Middle_east]
# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
# Group by interval and sum
result_df = subs.groupby('Interval').sum()
result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)
# Set the size of the heatmap
plt.figure(figsize=(25, 10)) # You can adjust the size as needed
# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Blues') # 'annot=False' hides the data values, 'cmap' sets the color map
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Middle East Speech Entities Over The Years Non Republican\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the frequency of specific entities mentioned in all Non-Republican, including whips and non denomination parties, President Speeches since US presidency specific to Middle Eastern Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
##
##
##
subs=letsgo[cleaned_list_Middle_east]
def map_to_interval(year):
return f"{(year // 10) * 10}-{(year // 10) * 10 + 9}"
# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
# Group by interval and sum
result_df = subs.groupby('Interval').sum()
result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)
# Set the size of the heatmap
plt.figure(figsize=(25, 10)) # You can adjust the size as needed
# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Purples') # 'annot=False' hides the data values, 'cmap' sets the color map
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Middle East Speech Entities Over The Years\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the frequency of specific entities mentioned in all President Speeches since US presidency specific to Middle East Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
# Assuming df is your DataFrame
# Initialize a Min-Max Scaler
scaler = MinMaxScaler()
# Scale the data
subs_scaled = pd.DataFrame(scaler.fit_transform(result_df), columns=result_df.columns, index=result_df.index)
# Create the heatmap
plt.figure(figsize=(25,10))
sns.heatmap(subs_scaled.T, annot=False, cmap='Purples')
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Middle East Speech Entities Over The Years MinMax Scaled\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the MinMax Scaled frequency of specific entities mentioned in all President Speeches since US presidency specific to Middle East Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
# Initialize a Standard Scaler
scaler = StandardScaler()
# Scale the data
letsgo_standardized = pd.DataFrame(scaler.fit_transform(result_df), columns=result_df.columns, index=result_df.index)
# Create the heatmap
plt.figure(figsize=(25, 10))
sns.heatmap(letsgo_standardized.T, annot=False, cmap='Purples')
plt.xlabel('Year', fontsize = 20)
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Middle East Speech Entities Over The Years Standardized\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the Standardized frequency of specific entities mentioned in all President Speeches since US presidency specific to Middle East Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/3291119174.py:75: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/3291119174.py:109: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/3291119174.py:145: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
Chart 5¶
data = [
"Vietnam: 473", "Japan: 387", "Viet-Nam: 145", "South Vietnam: 165",
"Korea: 174", "Philippines: 160", "Japanese: 157",
"South Vietnamese: 112", "South Viet-Nam: 111", "North Vietnam: 111",
"China: 841", "Chinese: 361"
]
# Splitting each string at ':' and taking the first part
# Splitting each string at ':' and taking the first part
cleaned_list_Asia = [item.split(':')[0] for item in data]
##
##
##
non_democrat_df = letsgo[letsgo['_Party_'] != 'Democrat']
non_Republican_df = letsgo[letsgo['_Party_'] != 'Republican']
import matplotlib.pyplot as plt
# Assuming non_democrat_df is your DataFrame
subs = cleaned_list_Asia
# Calculate the sum of each column
sums = []
for column in subs:
sums.append(non_democrat_df[column].sum())
# Create a bar chart using the sums
plt.bar(subs, sums, color='Red')
plt.xlabel('Entites', fontsize=20 )
plt.ylabel('Sum', fontsize=20)
plt.xticks(rotation=90, fontsize=15) # Rotate the x labels for better readability
plt.yticks( fontsize=15)
plt.title('Asian Entites Sum Non Democrat', fontsize=20)
# Add a caption
plt.figtext(0.5, -0.6, "The bar chart displays the frequency of specific entities mentioned in all Republican President Speeches since US presidency specific to Asian Involvement.", wrap=True, horizontalalignment='center', fontsize=13)
plt.show()
# Calculate the sum of each column
sums = []
for column in subs:
sums.append(non_Republican_df[column].sum())
# Create a bar chart using the sums
plt.bar(subs, sums, color='Blue')
plt.xlabel('Entites', fontsize=20 )
plt.ylabel('Sum', fontsize=20)
plt.xticks(rotation=90, fontsize=15) # Rotate the x labels for better readability
plt.yticks( fontsize=15)
plt.title('Asian Entites Sum Non Republican', fontsize=20)
# Add a caption
plt.figtext(0.5, -0.6, "The bar chart displays the frequency of specific entities mentioned in all Democrat President Speeches since US presidency specific to Asian Involvement.", wrap=True, horizontalalignment='center', fontsize=13)
plt.show()
def map_to_interval(year):
return f"{(year // 10) * 10}-{(year // 10) * 10 + 9}"
subs=non_democrat_df[cleaned_list_Asia]
# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
# Group by interval and sum
result_df = subs.groupby('Interval').sum()
result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)
# Set the size of the heatmap
plt.figure(figsize=(25, 10)) # You can adjust the size as needed
# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Reds') # 'annot=False' hides the data values, 'cmap' sets the color map
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Asia Speech Entities Over The Years Non Democrat\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the frequency of specific entities mentioned in all Non-Democratic, including whips and non denomination parties, President Speeches since US presidency specific to Asian Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
subs=non_Republican_df[cleaned_list_Asia]
# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
# Group by interval and sum
result_df = subs.groupby('Interval').sum()
result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)
# Set the size of the heatmap
plt.figure(figsize=(25, 10)) # You can adjust the size as needed
# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Blues') # 'annot=False' hides the data values, 'cmap' sets the color map
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Asia Speech Entities Over The Years Non Republican\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the frequency of specific entities mentioned in all Non-Republican, including whips and non denomination parties, President Speeches since US presidency specific to Asian Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
##
##
##
subs=letsgo[cleaned_list_Asia]
def map_to_interval(year):
return f"{(year // 10) * 10}-{(year // 10) * 10 + 9}"
# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
# Group by interval and sum
result_df = subs.groupby('Interval').sum()
result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)
# Set the size of the heatmap
plt.figure(figsize=(25, 10)) # You can adjust the size as needed
# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Purples') # 'annot=False' hides the data values, 'cmap' sets the color map
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Asia Speech Entities Over The Years\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the frequency of specific entities mentioned in all President Speeches since US presidency specific to Asian Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
# Assuming df is your DataFrame
# Initialize a Min-Max Scaler
scaler = MinMaxScaler()
# Scale the data
subs_scaled = pd.DataFrame(scaler.fit_transform(result_df), columns=result_df.columns, index=result_df.index)
# Create the heatmap
plt.figure(figsize=(25, 10))
sns.heatmap(subs_scaled.T, annot=False, cmap='Purples')
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Asia Speech Entities Over The Years MinMax Scaled\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the MinMax Scaled frequency of specific entities mentioned in all President Speeches since US presidency specific to Asian Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
# Initialize a Standard Scaler
scaler = StandardScaler()
# Scale the data
letsgo_standardized = pd.DataFrame(scaler.fit_transform(result_df), columns=result_df.columns, index=result_df.index)
# Create the heatmap
plt.figure(figsize=(25, 10))
sns.heatmap(letsgo_standardized.T, annot=False, cmap='Purples')
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Asia Speech Entities Over The Years Standardized\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the Standardized frequency of specific entities mentioned in all President Speeches since US presidency specific to Asian Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1824117806.py:73: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1824117806.py:107: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1824117806.py:143: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
Chart 6¶
data = [
"Israel: 332", "Palestinians: 70", "Palestinian: 68",
"Israelis: 62", "Palestine: 26", "Israeli: 58", "Jews: 34"
]
# Splitting each string at ':' and taking the first part
cleaned_list_I_P = [item.split(':')[0] for item in data]
##
##
##
non_democrat_df = letsgo[letsgo['_Party_'] != 'Democrat']
non_Republican_df = letsgo[letsgo['_Party_'] != 'Republican']
import matplotlib.pyplot as plt
# Assuming non_democrat_df is your DataFrame
subs = cleaned_list_I_P
# Calculate the sum of each column
sums = []
for column in subs:
sums.append(non_democrat_df[column].sum())
# Create a bar chart using the sums
plt.bar(subs, sums, color='Red')
plt.xlabel('Entites', fontsize=20 )
plt.ylabel('Sum', fontsize=20)
plt.xticks(rotation=45, fontsize=15) # Rotate the x labels for better readability
plt.yticks( fontsize=15)
plt.title('I/P Entites Sum Non Democrat', fontsize=20)
# Add a caption
plt.figtext(0.5, -0.4, "The bar chart displays the frequency of specific entities mentioned in all Republican President Speeches since US presidency specific to Isreal and Palestinian Involvement.", wrap=True, horizontalalignment='center', fontsize=13)
plt.show()
# Calculate the sum of each column
sums = []
for column in subs:
sums.append(non_Republican_df[column].sum())
# Create a bar chart using the sums
plt.bar(subs, sums, color='Blue')
plt.xlabel('Entites', fontsize=20 )
plt.ylabel('Sum', fontsize=20)
plt.xticks(rotation=45, fontsize=15) # Rotate the x labels for better readability
plt.yticks( fontsize=15)
plt.title('I/P Entites Sum Non Republican', fontsize=20)
# Add a caption
plt.figtext(0.5, -0.4, "The bar chart displays the frequency of specific entities mentioned in all Democrat President Speeches since US presidency specific to Israel and Palestinian Involvement.", wrap=True, horizontalalignment='center', fontsize=13)
plt.show()
def map_to_interval(year):
return f"{(year // 10) * 10}-{(year // 10) * 10 + 9}"
subs=non_democrat_df[cleaned_list_I_P]
# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
# Group by interval and sum
result_df = subs.groupby('Interval').sum()
result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)
# Set the size of the heatmap
plt.figure(figsize=(25, 7)) # You can adjust the size as needed
# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Reds') # 'annot=False' hides the data values, 'cmap' sets the color map
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('I/P Speech Entities Over The Years Non Democrat\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the frequency of specific entities mentioned in all Non-Democratic, including whips and non denomination parties, President Speeches since US presidency specific to Israel and Palestinian Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
subs=non_Republican_df[cleaned_list_I_P]
# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
# Group by interval and sum
result_df = subs.groupby('Interval').sum()
result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)
# Set the size of the heatmap
plt.figure(figsize=(25, 7)) # You can adjust the size as needed
# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Blues') # 'annot=False' hides the data values, 'cmap' sets the color map
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('I/P Speech Entities Over The Years Non Republican\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the frequency of specific entities mentioned in all Non-Republican, including whips and non denomination parties, President Speeches since US presidency specific to Israel and Palestinian Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
##
##
##
subs=letsgo[cleaned_list_I_P]
def map_to_interval(year):
return f"{(year // 10) * 10}-{(year // 10) * 10 + 9}"
# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
# Group by interval and sum
result_df = subs.groupby('Interval').sum()
result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)
# Set the size of the heatmap
plt.figure(figsize=(25, 7)) # You can adjust the size as needed
# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Purples') # 'annot=False' hides the data values, 'cmap' sets the color map
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('I/P Speech Entities Over The Years\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the frequency of specific entities mentioned in all President Speeches since US presidency specific to Israel and Palestinian Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
# Assuming df is your DataFrame
# Initialize a Min-Max Scaler
scaler = MinMaxScaler()
# Scale the data
subs_scaled = pd.DataFrame(scaler.fit_transform(result_df), columns=result_df.columns, index=result_df.index)
# Create the heatmap
plt.figure(figsize=(25,7))
sns.heatmap(subs_scaled.T, annot=False, cmap='Purples')
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('I/P Speech Entities Over The Years MinMax Scaled\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the MinMax Scaled frequency of specific entities mentioned in all President Speeches since US presidency specific to Israel and Palestinian Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
# Initialize a Standard Scaler
scaler = StandardScaler()
# Scale the data
letsgo_standardized = pd.DataFrame(scaler.fit_transform(result_df), columns=result_df.columns, index=result_df.index)
# Create the heatmap
plt.figure(figsize=(25, 7))
sns.heatmap(letsgo_standardized.T, annot=False, cmap='Purples')
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('I/P Speech Entities Over The Years Standardized\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the Standardized frequency of specific entities mentioned in all President Speeches since US presidency specific to Israel and Palestinian Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1824223548.py:69: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1824223548.py:103: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1824223548.py:139: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
Chart 7¶
data = [
"Negro: 84", "the Civil War: 56", "African American: 34",
"Negroes: 25", "Africans: 22", "African-American: 15",
"African-Americans: 15", "Native American: 15"
]
# Splitting each string at ':' and taking the first part
cleaned_list_Minority = [item.split(':')[0] for item in data]
##
##
##
non_democrat_df = letsgo[letsgo['_Party_'] != 'Democrat']
non_Republican_df = letsgo[letsgo['_Party_'] != 'Republican']
import matplotlib.pyplot as plt
# Assuming non_democrat_df is your DataFrame
subs = cleaned_list_Minority
# Calculate the sum of each column
sums = []
for column in subs:
sums.append(non_democrat_df[column].sum())
# Create a bar chart using the sums
plt.bar(subs, sums, color='Red')
plt.xlabel('Entites', fontsize=20 )
plt.ylabel('Sum', fontsize=20)
plt.xticks(rotation=75, fontsize=15) # Rotate the x labels for better readability
plt.yticks( fontsize=15)
plt.title('Minority Entites Sum Non Democrat', fontsize=20)
# Add a caption
plt.figtext(0.5, -0.6, "The bar chart displays the frequency of specific entities mentioned in all Republican President Speeches since US presidency specific to Minority Involvement.", wrap=True, horizontalalignment='center', fontsize=13)
plt.show()
# Calculate the sum of each column
sums = []
for column in subs:
sums.append(non_Republican_df[column].sum())
# Create a bar chart using the sums
plt.bar(subs, sums, color='Blue')
plt.xlabel('Entites', fontsize=20 )
plt.ylabel('Sum', fontsize=20)
plt.xticks(rotation=75, fontsize=15) # Rotate the x labels for better readability
plt.yticks( fontsize=15)
plt.title('Minority Entites Sum Non Republican', fontsize=20)
# Add a caption
plt.figtext(0.5, -0.6, "The bar chart displays the frequency of specific entities mentioned in all Democrat President Speeches since US presidency specific to Minority Involvement.", wrap=True, horizontalalignment='center', fontsize=13)
plt.show()
def map_to_interval(year):
return f"{(year // 10) * 10}-{(year // 10) * 10 + 9}"
subs=non_democrat_df[cleaned_list_Minority]
# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
# Group by interval and sum
result_df = subs.groupby('Interval').sum()
result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)
# Set the size of the heatmap
plt.figure(figsize=(25, 7)) # You can adjust the size as needed
# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Reds') # 'annot=False' hides the data values, 'cmap' sets the color map
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Minority Speech Entities Over The Years Non Democrat\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the frequency of specific entities mentioned in all Non-Democratic, including whips and non denomination parties, President Speeches since US presidency specific to Minority Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
subs=non_Republican_df[cleaned_list_Minority]
# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
# Group by interval and sum
result_df = subs.groupby('Interval').sum()
result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)
# Set the size of the heatmap
plt.figure(figsize=(25, 7)) # You can adjust the size as needed
# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Blues') # 'annot=False' hides the data values, 'cmap' sets the color map
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Minority Speech Entities Over The Years Non Republican\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the frequency of specific entities mentioned in all Non-Republican, including whips and non denomination parties, President Speeches since US presidency specific to Minority Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
##
##
##
subs=letsgo[cleaned_list_Minority]
def map_to_interval(year):
return f"{(year // 10) * 10}-{(year // 10) * 10 + 9}"
# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
# Group by interval and sum
result_df = subs.groupby('Interval').sum()
result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)
# Set the size of the heatmap
plt.figure(figsize=(25, 7)) # You can adjust the size as needed
# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Purples') # 'annot=False' hides the data values, 'cmap' sets the color map
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Minority Speech Entities Over The Years\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the frequency of specific entities mentioned in all President Speeches since US presidency specific to Minority Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
# Assuming df is your DataFrame
# Initialize a Min-Max Scaler
scaler = MinMaxScaler()
# Scale the data
subs_scaled = pd.DataFrame(scaler.fit_transform(result_df), columns=result_df.columns, index=result_df.index)
# Create the heatmap
plt.figure(figsize=(25,7))
sns.heatmap(subs_scaled.T, annot=False, cmap='Purples')
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Minority Speech Entities Over The Years MinMax Scaled\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the MinMax Scaled frequency of specific entities mentioned in all President Speeches since US presidency specific to Minority Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
# Initialize a Standard Scaler
scaler = StandardScaler()
# Scale the data
letsgo_standardized = pd.DataFrame(scaler.fit_transform(result_df), columns=result_df.columns, index=result_df.index)
# Create the heatmap
plt.figure(figsize=(25, 7))
sns.heatmap(letsgo_standardized.T, annot=False, cmap='Purples')
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Minority Speech Entities Over The Years Standardized\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the Standardized frequency of specific entities mentioned in all President Speeches since US presidency specific to Minority Involvement in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1375633772.py:71: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1375633772.py:105: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/1375633772.py:141: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
Chart 8¶
data = [
"the United Nations: 407", "Medicare: 252", "Ford: 190", "Kennedy: 276",
"Trump: 91", "Truman: 74", "Lincoln: 73", "Martin Luther King: 31",
"Bush: 163", "Reagan: 149", "Nixon: 147", "Pakistan: 81",
"Watergate: 73", "the Civil War: 56", "COVID-19: 62", "Great Britain: 679",
"British: 603"
]
# Splitting each string at ':' and taking the first part
cleaned_list_assorted = [item.split(':')[0] for item in data]
##
##
##
non_democrat_df = letsgo[letsgo['_Party_'] != 'Democrat']
non_Republican_df = letsgo[letsgo['_Party_'] != 'Republican']
import matplotlib.pyplot as plt
# Assuming non_democrat_df is your DataFrame
subs = cleaned_list_assorted
# Calculate the sum of each column
sums = []
for column in subs:
sums.append(non_democrat_df[column].sum())
# Create a bar chart using the sums
plt.bar(subs, sums, color='Red')
plt.xlabel('Entites', fontsize=20, labelpad=20 )
plt.ylabel('Sum', fontsize=20)
plt.xticks(rotation=90, fontsize=15) # Rotate the x labels for better readability
plt.yticks( fontsize=15)
plt.title('Other Entites Sum Non Democrat', fontsize=20)
# Add a caption
plt.figtext(0.5, -0.65, "The bar chart displays the frequency of specific entities mentioned in all Republican President Speeches since US presidency non-specific to any event or topic but that seemed of interest.", wrap=True, horizontalalignment='center', fontsize=13)
plt.show()
# Calculate the sum of each column
sums = []
for column in subs:
sums.append(non_Republican_df[column].sum())
# Create a bar chart using the sums
plt.bar(subs, sums, color='Blue')
plt.xlabel('Entites', fontsize=20, labelpad=20 )
plt.ylabel('Sum', fontsize=20)
plt.xticks(rotation=90, fontsize=15) # Rotate the x labels for better readability
plt.yticks( fontsize=15)
plt.title('Other Entites Sum Non Republican', fontsize=20)
# Add a caption
plt.figtext(0.5, -0.65, "The bar chart displays the frequency of specific entities mentioned in all Democrat President Speeches since US presidency non-specific to any event or topic but that seemed of interest.", wrap=True, horizontalalignment='center', fontsize=13)
plt.show()
def map_to_interval(year):
return f"{(year // 10) * 10}-{(year // 10) * 10 + 9}"
subs=non_democrat_df[cleaned_list_assorted]
# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
# Group by interval and sum
result_df = subs.groupby('Interval').sum()
result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)
# Set the size of the heatmap
plt.figure(figsize=(25, 10)) # You can adjust the size as needed
# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Reds') # 'annot=False' hides the data values, 'cmap' sets the color map
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Other Speech Entities Over The Years Non Democrat\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the frequency of specific entities mentioned in all Non-Democratic, including whips and non denomination parties, President Speeches since US presidency non-specific to any event or topic but that seemed of interest, in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
subs=non_Republican_df[cleaned_list_assorted]
# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
# Group by interval and sum
result_df = subs.groupby('Interval').sum()
result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)
# Set the size of the heatmap
plt.figure(figsize=(25, 10)) # You can adjust the size as needed
# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Blues') # 'annot=False' hides the data values, 'cmap' sets the color map
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Other Speech Entities Over The Years Non Republican\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the frequency of specific entities mentioned in all Non-Democratic, including whips and non denomination parties, President Speeches since US presidency non-specific to any event or topic but that seemed of interest, in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
##
##
##
subs=letsgo[cleaned_list_assorted]
def map_to_interval(year):
return f"{(year // 10) * 10}-{(year // 10) * 10 + 9}"
# Assign years to intervals
subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
# Group by interval and sum
result_df = subs.groupby('Interval').sum()
result_df.index= result_df.index.astype(str).str[:4]
result_df.rename_axis("year", inplace=True)
# Set the size of the heatmap
plt.figure(figsize=(25, 10)) # You can adjust the size as needed
# Create the heatmap
sns.heatmap(result_df.T, annot=False, cmap='Purples') # 'annot=False' hides the data values, 'cmap' sets the color map
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Other Speech Entities Over The Years\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the frequency of specific entities mentioned in all President Speeches since US presidency non-specific to any event or topic but that seemed of interest, in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
# Assuming df is your DataFrame
# Initialize a Min-Max Scaler
scaler = MinMaxScaler()
# Scale the data
subs_scaled = pd.DataFrame(scaler.fit_transform(result_df), columns=result_df.columns, index=result_df.index)
# Create the heatmap
plt.figure(figsize=(25, 10))
sns.heatmap(subs_scaled.T, annot=False, cmap='Purples')
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Other Speech Entities Over The Years MinMax Scaled\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the MinMax Scaled frequency of specific entities mentioned in all President Speeches since US presidency non-specific to any event or topic but that seemed of interest, in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
# Initialize a Standard Scaler
scaler = StandardScaler()
# Scale the data
letsgo_standardized = pd.DataFrame(scaler.fit_transform(result_df), columns=result_df.columns, index=result_df.index)
# Create the heatmap
plt.figure(figsize=(25, 10))
sns.heatmap(letsgo_standardized.T, annot=False, cmap='Purples')
plt.xlabel('Year', fontsize = 20)
plt.yticks(fontsize = 20, rotation = 360)
plt.xticks(fontsize = 17)
plt.title('Other Speech Entities Over The Years Standardized\n', fontsize=25)
# Add a caption
plt.figtext(0.4, -0.05, "The heatmap displays the Standardized frequency of specific entities mentioned in all President Speeches since US presidency non-specific to any event or topic but that seemed of interest, in a temporal dimension cut up by decades.", wrap=True, horizontalalignment='center', fontsize=20)
#plt.savefig('Speech_heatmap.png');
plt.show()
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2766536643.py:74: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2766536643.py:108: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/2766536643.py:144: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy subs['Interval'] = letsgo["_Year_"].astype(int).map(map_to_interval)
Case Studies¶
Relation Extraction¶
file=pd.read_csv("outputfinal_filename.csv")
file
| doc_name | date | transcript | president | title | Year | |
|---|---|---|---|---|---|---|
| 0 | april-30-1789-first-inaugural-address | 1789-04-30 | Fellow Citizens of the Senate and the House of... | George Washington | April 30, 1789: First Inaugural Address | 1789 |
| 1 | october-3-1789-thanksgiving-proclamation | 1789-10-03 | Whereas it is the duty of all Nations to ackno... | George Washington | October 3, 1789: Thanksgiving Proclamation | 1789 |
| 2 | january-8-1790-first-annual-message-congress | 1790-01-08 | Fellow Citizens of the Senate and House of Rep... | George Washington | January 8, 1790: First Annual Message to Congress | 1790 |
| 3 | december-8-1790-second-annual-message-congress | 1790-12-08 | Fellow citizens of the Senate and House of Re... | George Washington | December 8, 1790: Second Annual Message to Con... | 1790 |
| 4 | december-29-1790-talk-chiefs-and-counselors-se... | 1790-12-29 | I the President of the United States, by my o... | George Washington | December 29, 1790: Talk to the Chiefs and Coun... | 1790 |
| ... | ... | ... | ... | ... | ... | ... |
| 1056 | may-24-2022-remarks-school-shooting-uvalde-texas | 2022-05-25 | Good evening, fellow Americans.\r\n\r\nI had h... | Joe Biden | May 24, 2022: Remarks on School Shooting in Uv... | 2022 |
| 1057 | september-1-2022-remarks-continued-battle-soul... | 2022-09-02 | THE PRESIDENT: My fellow Americans, please, if... | Joe Biden | September 1, 2022: Remarks on the Continued Ba... | 2022 |
| 1058 | september-21-2022-speech-77th-session-united-n... | 2022-09-21 | Thank you. \r\n\r\nMr. President, Mr. Secretar... | Joe Biden | September 21, 2022: Speech before the 77th Ses... | 2022 |
| 1059 | february-7-2023-state-union-address | 2023-02-08 | Mr. Speaker. Madam Vice President. Our First L... | Joe Biden | February 7, 2023: State of the Union Address | 2023 |
| 1060 | february-21-2023-remarks-one-year-anniversary-... | 2023-02-22 | THE PRESIDENT: Hello, Poland! One of our grea... | Joe Biden | February 21, 2023: Remarks on the One-Year Ann... | 2023 |
1061 rows × 6 columns
This took really long and coldnt be run in Juypter, cause it didnt let me install Neuralcoref due to Juypter inability to install spacey 2.1.1, so made an environment through the terminal and was abel to install Neuracoref in conda¶
## i made a seperate python file that can be run in the folder woth this file
## Refer to the python file 'neurl_coref.py'
'''
!pip install spacey==2.1.1
!pip install neuralcoref
import spacy
import neuralcoref
import pandas as pd
file=pd.read_csv("outputfinal_filename.csv")
nlp = spacy.load('en_core_web_sm')
neuralcoref.add_to_pipe(nlp)
overall = pd.DataFrame(columns=['doc_name', 'date', 'transcript', 'president', 'title', 'Year'])
for i in range(len(file)):
prez=str(file.loc[i, "president"])
trans=str(file.loc[i, "transcript"])
prez= prez.replace(" ", "")
final= trans.replace('I ',""+prez+" " )
doc=nlp(final)
resolved_text = doc._.coref_resolved
overall.loc[i, 'doc_name']= file.loc[i, "doc_name"]
overall.loc[i, 'date']= file.loc[i, "date"]
overall.loc[i, 'transcript']= resolved_text
overall.loc[i, 'president']= file.loc[i, "president"]
overall.loc[i, 'title']= file.loc[i, "title"]
overall.loc[i, 'Year']= file.loc[i, "Year"]
print(""+str(i)+"/"+str(len(file))+"")
overall.reset_index(inplace=True)
overall.drop('index', axis=1, inplace=True)
overall.to_csv('neuraled.csv', index=False)
'''
'\n!pip install spacey==2.1.1\n!pip install neuralcoref\n\nimport spacy\nimport neuralcoref\nimport pandas as pd\n\nfile=pd.read_csv("outputfinal_filename.csv")\n\nnlp = spacy.load(\'en_core_web_sm\')\nneuralcoref.add_to_pipe(nlp)\n\noverall = pd.DataFrame(columns=[\'doc_name\', \'date\', \'transcript\', \'president\', \'title\', \'Year\'])\n\nfor i in range(len(file)):\n prez=str(file.loc[i, "president"])\n trans=str(file.loc[i, "transcript"])\n prez= prez.replace(" ", "")\n final= trans.replace(\'I \',""+prez+" " )\n doc=nlp(final)\n resolved_text = doc._.coref_resolved\n \n overall.loc[i, \'doc_name\']= file.loc[i, "doc_name"]\n overall.loc[i, \'date\']= file.loc[i, "date"]\n overall.loc[i, \'transcript\']= resolved_text\n overall.loc[i, \'president\']= file.loc[i, "president"]\n overall.loc[i, \'title\']= file.loc[i, "title"]\n overall.loc[i, \'Year\']= file.loc[i, "Year"]\n \n print(""+str(i)+"/"+str(len(file))+"")\n \n \n \n \noverall.reset_index(inplace=True)\noverall.drop(\'index\', axis=1, inplace=True) \noverall.to_csv(\'neuraled.csv\', index=False)\n\n'
file=pd.read_csv("neuraled.csv")
file
| doc_name | date | transcript | president | title | Year | |
|---|---|---|---|---|---|---|
| 0 | april-30-1789-first-inaugural-address | 1789-04-30 | Fellow Citizens of the Senate and the House of... | George Washington | April 30, 1789: First Inaugural Address | 1789 |
| 1 | october-3-1789-thanksgiving-proclamation | 1789-10-03 | Whereas it is the duty of all Nations to ackno... | George Washington | October 3, 1789: Thanksgiving Proclamation | 1789 |
| 2 | january-8-1790-first-annual-message-congress | 1790-01-08 | Fellow Citizens of the Senate and House of Rep... | George Washington | January 8, 1790: First Annual Message to Congress | 1790 |
| 3 | december-8-1790-second-annual-message-congress | 1790-12-08 | Fellow citizens of the Senate and House of Re... | George Washington | December 8, 1790: Second Annual Message to Con... | 1790 |
| 4 | december-29-1790-talk-chiefs-and-counselors-se... | 1790-12-29 | GeorgeWashington the President of the United ... | George Washington | December 29, 1790: Talk to the Chiefs and Coun... | 1790 |
| ... | ... | ... | ... | ... | ... | ... |
| 1056 | may-24-2022-remarks-school-shooting-uvalde-texas | 2022-05-25 | Good evening, fellow Americans.\r\n\r\nJoeBide... | Joe Biden | May 24, 2022: Remarks on School Shooting in Uv... | 2022 |
| 1057 | september-1-2022-remarks-continued-battle-soul... | 2022-09-02 | THE PRESIDENT: My fellow Americans, please, if... | Joe Biden | September 1, 2022: Remarks on the Continued Ba... | 2022 |
| 1058 | september-21-2022-speech-77th-session-united-n... | 2022-09-21 | Thank you. \r\n\r\nMr. President, Mr. Secretar... | Joe Biden | September 21, 2022: Speech before the 77th Ses... | 2022 |
| 1059 | february-7-2023-state-union-address | 2023-02-08 | Mr. Speaker. Madam Vice President. Our First L... | Joe Biden | February 7, 2023: State of the Union Address | 2023 |
| 1060 | february-21-2023-remarks-one-year-anniversary-... | 2023-02-22 | THE PRESIDENT: Hello, Poland! One of our grea... | Joe Biden | February 21, 2023: Remarks on the One-Year Ann... | 2023 |
1061 rows × 6 columns
This took really long¶
# refer to the python file "relation.py"
'''
file=pd.read_csv("neuraled.csv")
# Load spaCy model
nlp = spacy.load('en_core_web_sm')
relations = pd.DataFrame(columns=['entity1','entity2', 'sentiment', 'date', 'president', 'title', 'Year'])
for e in range(int(len(file))):
text = file.loc[e, 'transcript' ]
print(""+str(e)+"/"+str(int(len(file)))+"")
# Process the text
doc = nlp(text)
# Assuming we are interested in relations involving two proper nouns (entities)
entities = [ent for ent in doc if ent.pos_ == 'PROPN']
## precations to make sure two word entites are not being analysied seperately
p=0
# Extracting relationships and performing sentiment analysis
for i in range(len(entities)-1):
entity1 = entities[i]
# Example: Checking if entities appear in the same sentence
combined_entities = str(entity1) + ' ' + str(entities[i+1])
if p>0:
p=p-1
continue
k=0
if combined_entities in entity1.sent.text:
entity1= combined_entities
k=k+1
for j in range(i+1, len(entities)):
entity2 = entities[j]
if k==1:
k=k-1
p=p+1
continue
if (j < len(entities)-2):
# Example: Checking if entities appear in the same sentence
combined_entities = str(entity2) + ' ' + str(entities[j+1])
if combined_entities in entity2.sent.text:
entity2= combined_entities
# Example: Checking if entities appear in the same sentence
if entities[i].sent == entities[j].sent :
# Sentiment analysis on the sentence
sentiment = TextBlob(str(entities[i].sent)).sentiment
rel = pd.DataFrame({
'entity1': [entity1],
'entity2': [entity2],
'sentiment': [sentiment.polarity], # Polarity score: -1 (negative) to 1 (positive)
'date': [file.loc[e, 'date']],
'president': [file.loc[e, 'president']],
'title': [file.loc[e, 'title']],
'Year': [file.loc[e, 'Year']]
})
relations=pd.concat([relations,rel],ignore_index=True)
relations.reset_index(inplace=True)
relations.drop('index', axis=1, inplace=True)
relations.to_csv('relationships1.csv', index=False)
'''
'\nfile=pd.read_csv("neuraled.csv")\n# Load spaCy model\nnlp = spacy.load(\'en_core_web_sm\')\nrelations = pd.DataFrame(columns=[\'entity1\',\'entity2\', \'sentiment\', \'date\', \'president\', \'title\', \'Year\'])\n\nfor e in range(int(len(file))):\n \n text = file.loc[e, \'transcript\' ]\n print(""+str(e)+"/"+str(int(len(file)))+"")\n\n # Process the text\n doc = nlp(text)\n\n # Assuming we are interested in relations involving two proper nouns (entities)\n entities = [ent for ent in doc if ent.pos_ == \'PROPN\']\n\n \n ## precations to make sure two word entites are not being analysied seperately\n p=0\n # Extracting relationships and performing sentiment analysis\n for i in range(len(entities)-1):\n entity1 = entities[i]\n\n # Example: Checking if entities appear in the same sentence\n combined_entities = str(entity1) + \' \' + str(entities[i+1])\n \n if p>0:\n p=p-1\n continue\n k=0\n if combined_entities in entity1.sent.text:\n entity1= combined_entities\n k=k+1\n for j in range(i+1, len(entities)):\n entity2 = entities[j]\n\n if k==1:\n k=k-1\n p=p+1\n continue\n\n if (j < len(entities)-2):\n # Example: Checking if entities appear in the same sentence\n combined_entities = str(entity2) + \' \' + str(entities[j+1])\n\n if combined_entities in entity2.sent.text:\n entity2= combined_entities\n\n\n\n\n\n # Example: Checking if entities appear in the same sentence\n\n if entities[i].sent == entities[j].sent :\n # Sentiment analysis on the sentence\n sentiment = TextBlob(str(entities[i].sent)).sentiment\n \n \n rel = pd.DataFrame({\n \'entity1\': [entity1],\n \'entity2\': [entity2],\n\n \'sentiment\': [sentiment.polarity], # Polarity score: -1 (negative) to 1 (positive)\n \n \'date\': [file.loc[e, \'date\']],\n \n \'president\': [file.loc[e, \'president\']],\n \'title\': [file.loc[e, \'title\']],\n \'Year\': [file.loc[e, \'Year\']]\n })\n relations=pd.concat([relations,rel],ignore_index=True) \n \nrelations.reset_index(inplace=True)\nrelations.drop(\'index\', axis=1, inplace=True) \nrelations.to_csv(\'relationships1.csv\', index=False) \n\n'
# Remove setiments that equal 0
relate= pd.read_csv('relationships1.csv')
relate=relate.loc[relate["sentiment"]!= 0.0]
relate.reset_index(inplace=True)
relate.drop('index', axis=1, inplace=True)
relate.to_csv('relationships1.csv', index=False)
relate= pd.read_csv('relationships1.csv')
relate
| entity1 | entity2 | sentiment | date | president | title | Year | |
|---|---|---|---|---|---|---|---|
| 0 | GeorgeWashington | Country | 0.233333 | 1789-04-30 | George Washington | April 30, 1789: First Inaugural Address | 1789 |
| 1 | GeorgeWashington | GeorgeWashington | 0.233333 | 1789-04-30 | George Washington | April 30, 1789: First Inaugural Address | 1789 |
| 2 | GeorgeWashington | GeorgeWashington | 0.233333 | 1789-04-30 | George Washington | April 30, 1789: First Inaugural Address | 1789 |
| 3 | Country | GeorgeWashington | 0.233333 | 1789-04-30 | George Washington | April 30, 1789: First Inaugural Address | 1789 |
| 4 | Country | GeorgeWashington | 0.233333 | 1789-04-30 | George Washington | April 30, 1789: First Inaugural Address | 1789 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 497537 | Ukraine | — Ukraine | 0.044583 | 2023-02-22 | Joe Biden | February 21, 2023: Remarks on the One-Year Ann... | 2023 |
| 497538 | Ukraine | Ukraine | 0.044583 | 2023-02-22 | Joe Biden | February 21, 2023: Remarks on the One-Year Ann... | 2023 |
| 497539 | Ukraine — | Ukraine | 0.044583 | 2023-02-22 | Joe Biden | February 21, 2023: Remarks on the One-Year Ann... | 2023 |
| 497540 | United States | Allies | 0.027778 | 2023-02-22 | Joe Biden | February 21, 2023: Remarks on the One-Year Ann... | 2023 |
| 497541 | ’s | Americans | 0.350000 | 2023-02-22 | Joe Biden | February 21, 2023: Remarks on the One-Year Ann... | 2023 |
497542 rows × 7 columns
chart = pd.DataFrame(columns=['entity1','entity2', 'avg_sentiment', 'entity_count', 'connect_count'])
This took really long¶
'''
dup = relate.drop_duplicates(subset='entity1')
dup.reset_index(inplace=True)
dup.drop('index', axis=1, inplace=True)
for i in range(len(dup)):
print(str(i)+'/'+str(len(dup)))
st=dup.loc[i, "entity1"]
temp1=relate.loc[relate['entity1']== st]
num1=len(temp1)
dum = temp1.drop_duplicates(subset='entity2')
dum.reset_index(inplace=True)
dum.drop('index', axis=1, inplace=True)
for j in range(len(dum)):
sm=dum.loc[j, "entity2"]
temp2=temp1.loc[temp1['entity2']== sm]
avg_sent=(temp2['sentiment'].sum())/len(temp2)
num2=len(temp2)
rel = pd.DataFrame({
'entity1': [st],
'entity2': [sm],
'avg_sentiment': [avg_sent], # Polarity score: -1 (negative) to 1 (positive)
'entity_count' : [num1],
'connect_count': [num2]
})
chart=pd.concat([chart,rel],ignore_index=True)
chart.to_csv('chart.csv', index=False)
'''
'\ndup = relate.drop_duplicates(subset=\'entity1\')\ndup.reset_index(inplace=True)\ndup.drop(\'index\', axis=1, inplace=True)\n\nfor i in range(len(dup)):\n print(str(i)+\'/\'+str(len(dup)))\n st=dup.loc[i, "entity1"]\n temp1=relate.loc[relate[\'entity1\']== st]\n \n num1=len(temp1)\n \n \n \n dum = temp1.drop_duplicates(subset=\'entity2\')\n dum.reset_index(inplace=True)\n dum.drop(\'index\', axis=1, inplace=True)\n \n for j in range(len(dum)):\n sm=dum.loc[j, "entity2"]\n temp2=temp1.loc[temp1[\'entity2\']== sm]\n \n avg_sent=(temp2[\'sentiment\'].sum())/len(temp2)\n num2=len(temp2)\n \n rel = pd.DataFrame({\n \'entity1\': [st],\n \'entity2\': [sm],\n \'avg_sentiment\': [avg_sent], # Polarity score: -1 (negative) to 1 (positive)\n \'entity_count\' : [num1],\n \'connect_count\': [num2]\n })\n chart=pd.concat([chart,rel],ignore_index=True)\nchart.to_csv(\'chart.csv\', index=False) \n\n'
chart
| entity1 | entity2 | avg_sentiment | entity_count | connect_count |
|---|
chart= pd.read_csv('chart.csv')
chart
| entity1 | entity2 | avg_sentiment | entity_count | connect_count | |
|---|---|---|---|---|---|
| 0 | GeorgeWashington | Country | 0.079034 | 570 | 10 |
| 1 | GeorgeWashington | GeorgeWashington | 0.219698 | 570 | 141 |
| 2 | GeorgeWashington | Act | 0.123333 | 570 | 3 |
| 3 | GeorgeWashington | Almighty | 0.398333 | 570 | 3 |
| 4 | GeorgeWashington | Universe | 0.185667 | 570 | 5 |
| ... | ... | ... | ... | ... | ... |
| 147543 | Hundred | ’s | -0.500000 | 1 | 1 |
| 147544 | Madam First | Lady | 0.375000 | 4 | 1 |
| 147545 | Madam First | United States | 0.375000 | 4 | 1 |
| 147546 | Madam First | States | 0.375000 | 4 | 1 |
| 147547 | Madam First | Europe | 0.375000 | 4 | 1 |
147548 rows × 5 columns
chart_ordered=chart.sort_values(by='entity_count', ascending=False)
chart_ordered=chart_ordered.sort_values(["entity_count", "connect_count"],ascending=False )
chart_ordered.reset_index(inplace=True)
chart_ordered.drop('index', axis=1, inplace=True)
chart_ordered = chart_ordered[~chart_ordered.apply(lambda row: row['entity2'] in row['entity1'], axis=1)]
chart_ordered.reset_index(inplace=True)
chart_ordered.drop('index', axis=1, inplace=True)
chart_ordered.to_csv('chart_ordered.csv', index=False)
chart_ordered
| entity1 | entity2 | avg_sentiment | entity_count | connect_count | |
|---|---|---|---|---|---|
| 0 | United States | Government | 0.093470 | 40135 | 1574 |
| 1 | United States | America | 0.115637 | 40135 | 969 |
| 2 | United States | President | 0.084311 | 40135 | 901 |
| 3 | United States | Congress | 0.102111 | 40135 | 714 |
| 4 | United States | Union | 0.088627 | 40135 | 467 |
| ... | ... | ... | ... | ... | ... |
| 141863 | Applied Physics | Lab | 0.200000 | 1 | 1 |
| 141864 | List | COVID-19 | 0.107143 | 1 | 1 |
| 141865 | thatHe | ’s | 0.800000 | 1 | 1 |
| 141866 | militaryHe | ’s | -0.100000 | 1 | 1 |
| 141867 | Q:—to Rick | Bright | 0.700000 | 1 | 1 |
141868 rows × 5 columns
Messing Around with Data starting here to figure out how to represent the knowledge maps¶
charted= pd.read_csv('chart_ordered.csv')
charted
| entity1 | entity2 | avg_sentiment | entity_count | connect_count | |
|---|---|---|---|---|---|
| 0 | United States | Government | 0.093470 | 40135 | 1574 |
| 1 | United States | America | 0.115637 | 40135 | 969 |
| 2 | United States | President | 0.084311 | 40135 | 901 |
| 3 | United States | Congress | 0.102111 | 40135 | 714 |
| 4 | United States | Union | 0.088627 | 40135 | 467 |
| ... | ... | ... | ... | ... | ... |
| 141863 | Applied Physics | Lab | 0.200000 | 1 | 1 |
| 141864 | List | COVID-19 | 0.107143 | 1 | 1 |
| 141865 | thatHe | ’s | 0.800000 | 1 | 1 |
| 141866 | militaryHe | ’s | -0.100000 | 1 | 1 |
| 141867 | Q:—to Rick | Bright | 0.700000 | 1 | 1 |
141868 rows × 5 columns
this takes a little bit of time
months=['January', 'February', 'March', 'April', 'May', 'June',
'July', 'August', 'September', 'October', 'November', 'December']
count=0
for i in range(len(charted)):
jack=str(charted.loc[i,'entity2'])
for month in months:
if month in jack :
charted = charted.drop(i)
#count+=1
charted
| entity1 | entity2 | avg_sentiment | entity_count | connect_count | |
|---|---|---|---|---|---|
| 0 | United States | Government | 0.093470 | 40135 | 1574 |
| 1 | United States | America | 0.115637 | 40135 | 969 |
| 2 | United States | President | 0.084311 | 40135 | 901 |
| 3 | United States | Congress | 0.102111 | 40135 | 714 |
| 4 | United States | Union | 0.088627 | 40135 | 467 |
| ... | ... | ... | ... | ... | ... |
| 141863 | Applied Physics | Lab | 0.200000 | 1 | 1 |
| 141864 | List | COVID-19 | 0.107143 | 1 | 1 |
| 141865 | thatHe | ’s | 0.800000 | 1 | 1 |
| 141866 | militaryHe | ’s | -0.100000 | 1 | 1 |
| 141867 | Q:—to Rick | Bright | 0.700000 | 1 | 1 |
138663 rows × 5 columns
charted.reset_index(inplace=True)
charted.drop('index', axis=1, inplace=True)
charted
| entity1 | entity2 | avg_sentiment | entity_count | connect_count | |
|---|---|---|---|---|---|
| 0 | United States | Government | 0.093470 | 40135 | 1574 |
| 1 | United States | America | 0.115637 | 40135 | 969 |
| 2 | United States | President | 0.084311 | 40135 | 901 |
| 3 | United States | Congress | 0.102111 | 40135 | 714 |
| 4 | United States | Union | 0.088627 | 40135 | 467 |
| ... | ... | ... | ... | ... | ... |
| 138658 | Applied Physics | Lab | 0.200000 | 1 | 1 |
| 138659 | List | COVID-19 | 0.107143 | 1 | 1 |
| 138660 | thatHe | ’s | 0.800000 | 1 | 1 |
| 138661 | militaryHe | ’s | -0.100000 | 1 | 1 |
| 138662 | Q:—to Rick | Bright | 0.700000 | 1 | 1 |
138663 rows × 5 columns
charted.loc[charted['avg_sentiment']<-0.9]
| entity1 | entity2 | avg_sentiment | entity_count | connect_count | |
|---|---|---|---|---|---|
| 635 | United States | Ukraine — | -1.0 | 40135 | 4 |
| 636 | United States | — Ukraine | -1.0 | 40135 | 4 |
| 13160 | Union | Korea | -1.0 | 3623 | 1 |
| 13161 | Union | U.S. | -1.0 | 3623 | 1 |
| 13162 | Union | North Korea | -1.0 | 3623 | 1 |
| ... | ... | ... | ... | ... | ... |
| 136833 | Paul II | God | -1.0 | 2 | 1 |
| 137057 | Nineteen | Nancy | -1.0 | 2 | 1 |
| 137058 | Nineteen | RonaldReagan | -1.0 | 2 | 1 |
| 137504 | Life Synagogue | Pittsburgh | -1.0 | 1 | 1 |
| 138202 | Beast | Belsen | -1.0 | 1 | 1 |
105 rows × 5 columns
# reove counts and connections less than 30, reomove entite\y2 which are substrings of entity1
chartoy=charted.loc[charted['entity_count']>29]
chartoy=chartoy.loc[charted['connect_count']>29]
chartoy.reset_index(inplace=True)
chartoy.drop('index', axis=1, inplace=True)
chartoy = chartoy[~chartoy.apply(lambda row: row['entity2'] in row['entity1'], axis=1)]
chartoy.reset_index(inplace=True)
chartoy.drop('index', axis=1, inplace=True)
chartoy.to_csv('chart_ordy.csv', index=False)
chartoy
| entity1 | entity2 | avg_sentiment | entity_count | connect_count | |
|---|---|---|---|---|---|
| 0 | United States | Government | 0.093470 | 40135 | 1574 |
| 1 | United States | America | 0.115637 | 40135 | 969 |
| 2 | United States | President | 0.084311 | 40135 | 901 |
| 3 | United States | Congress | 0.102111 | 40135 | 714 |
| 4 | United States | Union | 0.088627 | 40135 | 467 |
| ... | ... | ... | ... | ... | ... |
| 1246 | Export | Import Bank | 0.126172 | 86 | 30 |
| 1247 | Soviet Premier | Khrushchev | 0.002520 | 83 | 31 |
| 1248 | President López | Obrador | 0.190157 | 70 | 42 |
| 1249 | Choctaw | Chickasaw | 0.196028 | 70 | 35 |
| 1250 | Fifty | Congress | 0.247037 | 65 | 30 |
1251 rows × 5 columns
chartoy.loc[chartoy['avg_sentiment']<0]
| entity1 | entity2 | avg_sentiment | entity_count | connect_count | |
|---|---|---|---|---|---|
| 50 | United States | Island | -0.016570 | 40135 | 66 |
| 72 | United States | Rhode Island | -0.044448 | 40135 | 53 |
| 96 | United States | Departments | -0.010184 | 40135 | 39 |
| 100 | United States | Minister | -0.031579 | 40135 | 38 |
| 104 | United States | Affairs | -0.034864 | 40135 | 35 |
| 122 | United States | Foreign Affairs | -0.046774 | 40135 | 31 |
| 272 | President | Island | -0.032758 | 14738 | 51 |
| 275 | President | Rhode Island | -0.045748 | 14738 | 49 |
| 278 | President | Executive | -0.028899 | 14738 | 46 |
| 331 | State | Affairs | -0.036293 | 8538 | 35 |
| 332 | State | France | -0.025873 | 8538 | 35 |
| 341 | State | Minister | -0.051667 | 8538 | 30 |
| 342 | State | Foreign Affairs | -0.051667 | 8538 | 30 |
| 365 | Secretary | France | -0.035937 | 7244 | 32 |
| 366 | Secretary | Foreign Affairs | -0.050968 | 7244 | 31 |
| 369 | Secretary | Minister | -0.017742 | 7244 | 31 |
| 475 | House | Island | -0.050035 | 3840 | 49 |
| 476 | House | Rhode Island | -0.058333 | 3840 | 48 |
| 480 | House | Executive | -0.047724 | 3840 | 34 |
| 513 | Treasury | Bank | -0.036632 | 3369 | 35 |
| 525 | Republic | Cuba | -0.035573 | 3356 | 47 |
| 718 | Executive | Island | -0.013556 | 1527 | 55 |
| 719 | Executive | Rhode Island | -0.017356 | 1527 | 54 |
| 937 | JamesBuchanan | President | -0.026490 | 885 | 34 |
| 1061 | Rhode Island | Executive | -0.049221 | 581 | 53 |
| 1062 | Rhode Island | States | -0.047956 | 581 | 35 |
| 1084 | Nam | America | -0.018788 | 550 | 33 |
| 1085 | Nam | Russia | -0.006875 | 550 | 32 |
| 1086 | Nam | India | -0.006875 | 550 | 32 |
| 1087 | Nam | China | -0.006875 | 550 | 32 |
| 1163 | Minister | France | -0.037857 | 290 | 34 |
| 1164 | Minister | Affairs | -0.028800 | 290 | 33 |
| 1165 | Minister | Foreign Affairs | -0.044330 | 290 | 32 |
| 1181 | Ukraine | Warsaw | -0.262366 | 249 | 31 |
| 1189 | Foreign Affairs | France | -0.037857 | 236 | 34 |
| 1232 | Osama bin | Laden | -0.043686 | 120 | 40 |
| 1242 | t Senator | Kennedy | -0.038372 | 91 | 44 |
chartoy = chartoy.drop_duplicates(subset='entity1')
chartoy.reset_index(inplace=True)
chartoy.drop('index', axis=1, inplace=True)
chartoy
/var/folders/4k/4vfxm2nd59b749g493qd5bkr0000gn/T/ipykernel_1452/3296870759.py:3: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
chartoy.drop('index', axis=1, inplace=True)
| entity1 | entity2 | avg_sentiment | entity_count | connect_count | |
|---|---|---|---|---|---|
| 0 | United States | Government | 0.093470 | 40135 | 1574 |
| 1 | Congress | States | 0.101565 | 15251 | 1472 |
| 2 | Government | States | 0.101808 | 15089 | 1975 |
| 3 | President | States | 0.093720 | 14738 | 1686 |
| 4 | State | States | 0.077760 | 8538 | 1044 |
| ... | ... | ... | ... | ... | ... |
| 244 | Export | Bank | 0.120257 | 86 | 33 |
| 245 | Soviet Premier | Khrushchev | 0.002520 | 83 | 31 |
| 246 | President López | Obrador | 0.190157 | 70 | 42 |
| 247 | Choctaw | Chickasaw | 0.196028 | 70 | 35 |
| 248 | Fifty | Congress | 0.247037 | 65 | 30 |
249 rows × 5 columns
original_rows_to_remove = [249, 245, 236, 232, 221, 188, 177, 160, 129, 128, 118, 115, 110, 105, 90, 84, 76, 75, 69, 56, 54, 52, 48, 39, 38, 36, 31, 24]
# Adjusting to 0-based indexing by subtracting 1 from each index
rows_to_remove = [x - 1 for x in original_rows_to_remove]
# Remove the specified rows
chartoy = chartoy.drop(rows_to_remove)
# Optional: Reset index if you want a continuous index after removal
chartoy = chartoy.reset_index(drop=True)
chartoy
| entity1 | entity2 | avg_sentiment | entity_count | connect_count | |
|---|---|---|---|---|---|
| 0 | United States | Government | 0.093470 | 40135 | 1574 |
| 1 | Congress | States | 0.101565 | 15251 | 1472 |
| 2 | Government | States | 0.101808 | 15089 | 1975 |
| 3 | President | States | 0.093720 | 14738 | 1686 |
| 4 | State | States | 0.077760 | 8538 | 1044 |
| ... | ... | ... | ... | ... | ... |
| 216 | Nancy | RonaldReagan | 0.154602 | 89 | 34 |
| 217 | Reconstruction Finance | Corporation | 0.172128 | 89 | 31 |
| 218 | Soviet Premier | Khrushchev | 0.002520 | 83 | 31 |
| 219 | President López | Obrador | 0.190157 | 70 | 42 |
| 220 | Choctaw | Chickasaw | 0.196028 | 70 | 35 |
221 rows × 5 columns
Plotting Knowdlege map of the¶
def ploting_map(data_new, stwing= "Knowledge Map of Entity Relationships"):
# Creating a new graph
G_new = nx.Graph()
# Extracting all unique entities
all_entities_new = set(data_new['entity1']).union(set(data_new['entity2']))
# Creating a dictionary for entity sizes
default_size = 100 # Default size
entity_sizes_new = {entity: data_new[data_new['entity1'] == entity]['entity_count'].iloc[0]
if entity in data_new['entity1'].values else default_size
for entity in all_entities_new}
# Adding nodes with their sizes, scaled 5 times bigger
for entity, size in entity_sizes_new.items():
G_new.add_node(entity, size=size * 50) # Scaling the node size by 5 times
# Adding edges with varying thickness and color based on sentiment
for _, row in data_new.iterrows():
width = abs(row['avg_sentiment']) * 10 # Scale the width
color = 'red' if row['avg_sentiment'] < 0 else 'blue'
G_new.add_edge(row['entity1'], row['entity2'], weight=width, color=color)
# Extracting sizes for nodes, scaled 5 times bigger
sizes_new_scaled = [G_new.nodes[node]['size']/200 for node in G_new.nodes]
# Extracting colors and weights for edges
edge_colors_new = nx.get_edge_attributes(G_new,'color').values()
edge_weights_new = nx.get_edge_attributes(G_new,'weight').values()
pos = nx.spring_layout(G_new, k=1.5, iterations=20)
# Drawing the graph with scaled node sizes
plt.figure(figsize=(10, 8))
nx.draw(G_new,pos, with_labels=True, node_size=sizes_new_scaled, width=list(edge_weights_new), edge_color=list(edge_colors_new), node_color='skyblue', edgecolors='black')
plt.title(stwing)
plt.show()
ploting_map(charted.loc[charted['avg_sentiment']<0][:100], "100-150 negative sentiment over .75")
ploting_map(charted.loc[charted['avg_sentiment']<0][:400], "First 400 negative Sent. Relation Knowledge Map")
ploting_map(chartoy.loc[chartoy['avg_sentiment']<0], "all negative Sent. with counts more than 30")
ploting_map(chartoy.loc[chartoy['avg_sentiment']>0.25], "top positive Sent. of 0ver 0.25 Relation Knowledge Map top relation for each entity")
The End